Sarofim, M. C M G Schultz

Inostroza, L M Palme

The Effects of Air Pollutants on Vegetation and the Role of Vegetation in Reducing

Keywords: Air quality, modeling, urban, diffusion, high-resolution

Nowadays, more than half of the world's population lives in urban areas, air quality prediction in urban areas is therefore flagship topic. The improvement of mesoscale chemical transport models generally focused on the dynamics, physical parameterization processes, and chemical reaction mechanisms. In the first part of the study, three configurations of vertical diffusion coefficient (Kz) based on fisrt order closure K-theory are tested within CHIMERE model fed by IFS meteorology over one year.

Although, all these Kz configurations are known to be efficient for mesoscale air quality simulations and the results show small differences between the three schemes, they all overestimated vertical mixing in urban areas. It is worth noting that the model used overestimates the ratio of PM2.5/PM10 by approximately 20% for all seasons. Discrepancies on emission inventories could be a relevant explanation. Many studies on the impact of physical parameterizations indicates the need of more accurate simulations of meteorological conditions for air quality modeling at urban scale. Also, for the second part of this work, the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE has been used to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region.

Large differences were found between different canopy schemes, showing an overall overestimation of concentrations during the pollution episode. The boundary layer schemes sensitivity tests display an 18.77%, 24.51% and 9.52% in Paris, Lyon and Bordeaux, respectively. PM2.5 and PM10 simulation provided similar results with NO 2 , 13.47% and 19.08% improvements were found in the urban area of Paris. The simulation with more realistic eddy viscosities is better than initial Kz parameterization which has been widely used in CTM. This work illuminates the user to identify the best settings of atmospheric models for appropriate operational uses to simulate the air quality during pollution episodes or for long term analyses in urban areas.

Ré sumé

De nos jours, plus de la moitié de la population mondiale vit dans des zones urbaines. La pré vision et l'analyse de la qualité de l'air dans les zones urbaines est un sujet d'actualité. L'amélioration des modè les de transport chimique à mé so-é chelle passe gé né ralement par le dé veloppement de paramé trisations plus avancé es en termes de dynamique et mé canismes chimiques. Dans la premiè re partie de l'é tude, trois configurations du coefficient de diffusion verticale (Kz) basé es sur la thé orie de fermeture de premier ordre (k-theory) sont testé es avec le modè le CHIMERE alimenté par les donné es Europé ennes mé té orologiques IFS pendant une anné e. Ces configurations, bien que jugé es efficaces pour les simulations de qualité de l'air à mé so-é chelle, montrent une surestimation significative du mé lange vertical dans les zones urbaines avec des diffé rences faibles entre les trois sché mas. Il convient de noter que le modè le ainsi configuré a surestimé le rapport PM2.5/PM10 d'environ 20% quelle que soit la saison. L'é tude de l'impact de la paramé trisation de la diffusion verticale confirme bien que la modé lisation mé té orologique est d'une importance majeure pour la modé lisation de la qualité de l'air. Par la suite, le modè le Weather Research and Forecast (WRF) couplé au modè le de transport de la chimie (CTM) CHIMERE a é té utilisé pour comprendre l'impact des paramé trisations physiques sur la simulation de la qualité de l'air sur é pisode de pollution en la ré gion parisienne. Les diffé rents sché mas de canopé e testé s montrent des diffé rences importantes avec une surestimation globale des concentrations pendant l'é pisode de pollution. Les tests de sensibilité des sché mas de la couche limite montrent une sous-estimation de la hauteur de la couche limite qui a pour consé quence une forte surestimation de la concentration des polluants pendant l'é pisode de pollution. Le sché ma de Boulac-BEP montre des performances significativement meilleures que les autres sché mas pour la simulation de cette hauteur de couche limite ainsi que pour les concentrations en polluants, ce qui confirme bien que les sché mas de canopé e urbaine et de couche limite ont un effet critique sur la modé lisation de la qualité de l'air dans la ré gion urbaine. La troisiè me partie de ce travail porte sur l'impact de la ré solution verticale de la grille et la hauteur de la premiè re couche qui jouent é galement un rôle trè s important dans la modé lisation du transport. Les trois configurations testé es dé montrent les capacité s du modè le à simuler des journé es normales hors é pisodes de pollution. Cependant, lors d'un é pisode de pollution, l'affinement de la ré solution verticale et de la hauteur de la premiè re couche entraî ne une nette amé lioration des ré sultats de modé lisation en comparaison avec la configuration initiale du modè le. La simulation et la pré vision de la qualité de l'air en ré gion urbaine sont donc influencé es par les caracté ristiques de la canopé e urbaine, la couche limite et les conditions de diffusion, le modè le de surface terrestre ayant un lé ger impact sur la simulation mé té orologique et la qualité de l'air. Dans la dernière partie de ce travail, la diffusivité turbulente de WRF issue d'un sché ma de fermeture de la diffusivité d'ordre 1.5 basé sur l'é nergie ciné tique de la turbulence est inté gré e dans CHIMERE afin de repré senter un mé lange vertical plus ré aliste prè s de la surface pour les applications de pollution urbaine. Une simulation haute ré solution de 15 jours en hiver a é té ré alisé e sur Paris, Lyon et Bordeaux avec des ré solutions horizontale et verticale fines de 1,67km et 12m respectivement. Il est à noter que la nouvelle diffusion verticale (NED) a amé lioré la simulation du NO 2 pour chaque site urbain par rapport à la diffusion Kz initiale (IKD). Les amé liorations moyennes en termes de RMSE sont de 18,77%, 24,51% et 9,52% à Paris, Lyon et Bordeaux, respectivement. La simulation des PM2.5 et PM10 a donné les mê mes ré sultats que celle du NO 2 , avec des amé liorations de 13,47% et 19,08% respectivement dans la zone urbaine de Paris. La simulation avec un coefficient de diffusion turbulente plus réaliste est meilleure qu'avec le Kz initial utilisé dans le CTM.

Outre l'analyse de paramétrisations à l'échelle urbaine et leur influence sur la qualité de l'air à l'échelle urbaine, Ce travail de thè se permet donc à l'utilisateur d'identifier plus facilement les paramètres essentiels pour la configuration des modèles atmosphériques dans l'objectif d'une utilisation opé rationnelle optimisé e afin de simuler la qualité de l'air pendant les é pisodes de pollution ou pour des analyses sur le long terme en zone urbaine. Mots-clé s : Qualité de l'air ; modé lisation ; urbain ; diffusion ; haute ré solution underestimation of the boundary layer height which leads to a strong overestimations of pollutants concentration. The Boulac-BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and the pollutants concentrations, indicating that both the canopy schemes and boundary layer schemes have a critical effect on air quality prediction in the urban region. The third part of this work focuses on the vertical resolution of the grid and the height of the first layer which also plays a very important role in the transport modeling. The three configurations tested demonstrate the model's ability to simulate regular days without pollution episodes. However, during a pollution episode, the refinement of the vertical resolution and of the height of the first layer leads to a clear improvement of the modeling results compared to the initial configuration of the model. The air quality predictions in urban regions are mainly influenced by the urban canopy, the boundary layer and the diffusion conditions. The land surface model had a slight impact for both meteorological and air quality simulation. In the last part of this work, the eddy diffusion of WRF from a 1.5 order diffusivity closure scheme based on the kinetic energy of turbulence is directly integrated into CHIMERE in order to represent a more realistic vertical mixing near the surface for urban pollution applications. A two weeks high resolution simulation was performed in Paris, Lyon and Bordeaux with finest horizontal and vertical resolution of 1.67km and 12m respectively. The new eddy diffusion (NED) integrated in this research improves NO 2 simulations at all urban site compared to the original Kz diffusion (IKD) on board CHIMERE. The average improvements in terms of RMSE are

Air pollutants in the Atmosphere

The atmosphere of Earth is schematically divided into four different sub layers: the troposphere, the stratosphere, the mesosphere, and the thermosphere, each of them with different chemical and physical properties (Mohanakumar, 2008;[START_REF] Williams | Analysis of long-period waves using the mesosphere-stratosphere-troposphere radar at Poker Flat[END_REF].

The troposphere is the lowest layer of the atmosphere which extends approximately up to 10 to 15km from the surface of Earth and contains most of the atmosphere's mass and human activities. Fossil fuels such as oil and coal have been powering the industrial development and daily life since the first industrial revolution (Najjar, 2011). Anthropogenic emissions of gaseous and particulate matters changed the share of many species in the atmosphere, causing air pollution, climate change and other environmental issues (Meng et al., 2009). Gaseous pollutants can be divided into primary pollutants and secondary pollutants (Nazaroff and Weschler, 2004). Primary pollutants refer to the original pollutants discharged directly from the pollution source into the atmosphere (Fisher, 2017). Secondary pollutants refer to a series of chemical or photochemical reactions between primary pollutants and existing components in the atmosphere, or several primary pollutants [START_REF] Jenkin | Ozone and other secondary photochemical pollutants: chemical processes governing their formation in the planetary boundary layer[END_REF]. Primary pollutants that have received general attention in air pollution control include sulfur oxides, nitrogen oxides, carbon oxides and organic compounds; secondary pollutants include sulfuric acid and photochemical compounds (WHO, 2006).

The presence in the atmosphere of particles with different sizes, which are usually defined as aerosols with their specific chemical composition, is of particular interest. They are ubiquitous in the Earth's atmosphere, but their distribution patterns are extremely variable, due to complex cycles of different aerosol types from various sources and with different lifetimes from a few days to a few weeks (Seinfeld and Pandis, 2016). Some aerosols can be directly emitted from both anthropogenic emissions (traffic emissions, industrial activities, biomass burning, etc.) and natural sources (vegetation, volcanoes, oceans, deserts, etc.) which are called primary particles, or later formed by physical and chemical mechanisms like the condensation of low-volatility gases, nucleation or chemical reactions, described as secondary particles (Hallquist et al., 2009). The size and chemical composition of an aerosol mixture present in the atmosphere is determined by its source and formation pathways (Colbeck and Chapter I 3/191 Lazaridis, 2010). As shown in Figure1.1, particles with an aerodynamic diameter greater than 2.5μm are usually produced by mechanical processes (coarse mode) whereas for nucleation mode particles (less than 10nm) homogeneous nucleation is the main source (Xu et al., 2011).

In between these size ranges, at 10-100nm (Aitken mode) and 0.1-2.5μm (accumulation mode), particles are generated via coagulation from the nucleation mode which can further grow through condensation (Finlayson-Pitts and Pitts.Jr, 1999).

Figure1.1 Typical sources, formation, removal pathways and size distributions of atmospheric aerosol (Finlayson-Pitts and Pitts.Jr, 1999).

Effects of air pollution on climate

Human activities, particularly fuel combustion increase the concentration of greenhouse gases, leading to global warming. This has also led to rising sea levels and melting of glaciers caps.

As the temperature continues to rise, more environmental changes will inevitably occur (Bai et al., 2018). Studies have demonstrated that the average global temperature is expected to rise by 2 °C to 4.5 °C while the sea levels will increase about 28 cm until 2100 (Desonie, 2007;[START_REF] Kemp | Climate related sea-level variations over the past two millennia[END_REF]. Aerosols also have significant impacts in climate change (Prather, Chapter I 4/191 2009). These impacts are generally described in terms of radiative forcing (RF) ( Bond et al., 2013;Charlson et al., 1992;IPCC 2013;[START_REF] Kok | A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle[END_REF]Scott et al., 2018). Some pollutants mainly scatter solar radiation have a direct cooling effect, associated to negative RF (Myhre et al., 2013). In contrast, absorbing pollutants have a direct warming effect, it could darken the surface of snow and ice, reducing their albedo then hastening melting [START_REF] Hansen | Efficacy of climate forcings[END_REF].

The largest contribution to RF is caused by increasing fossil fuel combustion, mostly of carbon dioxide (Andres et al., 2012). Those greenhouse gases and particles increased as a result of human activities especially in the past century. As illustrated by Figure1.2, semi-direct and indirect effects are also important processes on climate changes. The semi-direct effect corresponds to rapid adjustments induced by aerosol radiative effects on the surface energy. It enhances the heating of the surrounding air while reducing the amount of solar radiation reaching the ground that can stabilize the atmosphere and reduce convection, it could also increase the atmospheric temperature which reduce the relative humidity, inhibit cloud formation, and enhance the evaporation of existing clouds (Myhre et al., 2013).

Figure1.2 Direct, semi-direct and indirect effects of aerosols on climate(IPCC. 2013).

Figure1.3 summarizes the mean net RF estimated for the major air pollutants with uncertainty levels -as compared to reference conditions prevailing before the industrial age. Gaseous species globally display an increase of positive RF, uncertainties remains low (Shindell et al., 2009). In contrast, climate change effects caused by aerosols are strongly associated with their chemical composition, with opposite influences, as well as much higher uncertainties. For example, inorganic species like nitrate and sulfate, globally exhibit negative RF, while black Chapter I 5/191 carbon shows a strong positive RF through aerosol -radiation interactions organic aerosol are associated with a net negative RF at global scale (Corbera et al., 2016). However, it could also participate in local to regional positive RF due to brown carbon light absorption (Liu et al., 2017). Overall, with the breakthrough in observation measurements and atmospheric model, researchers made a great improvement in the understanding of characteristics of atmospheric aerosols and their climate effect over the last decades, uncertainty in the total direct aerosol effect is reduced based on a combination of global aerosol models and observation-based methods. But for a single component of particulate matter, especially the influence of carbonaceous aerosol, the effect on RF remains uncertain. Meanwhile, as shown in Figure1.4, aerosols optical depth obtained from remote sensing is highly inhomogeneous, and the global aerosol composition varies greatly in space and time (Remer et al., 2008). Therefore, the regional differences in the impact of aerosols on climate change and the propagation process are also worthy of more in-depth study. Overall, effective climate change mitigation and adaptation strategies depend on the enhancement of our understanding of their sources and optical properties.

Figure1.3 Change in RF effects caused by atmospheric pollutants for the period 1750-2011, associated with individual confidence range (IPCC, 2013) Figure1.4 MODIS aerosol optical depth averaged over the 10-year period 2001-2010Pie charts shows the contribution of different aerosols to the total aerosol optical depth for different regions (Myhre et al., 2013).

Health effect of air pollution

Ambient air pollution is a complex mixture of aerosols and gases. PM2.5 and ozone are two important indicators for air quality (White, 2009;You et al., 2018). PM2.5 is a consistent and robust predictor for health effects from studies of long-term exposure to air pollution (Danesh Yazdi et al., 2020;Kloog et al., 2013). Studies indicate that a 10μg/m 3 increase in ambient PM2.5 was expected to increase admissions in hospitals by 4.5% [START_REF] Kwon | Association of short-and long-term exposure to air pollution with atrial fibrillation[END_REF]. Ozone has been associated with increased respiratory mortality (Zhang et al., 2019) According to the World Health Organization (WHO), air pollution represents one of the greatest risks to public health (WHO., 2016), it attributes 3.7 million premature deaths to Chapter I 7/191 outdoor air pollution and 4.3 million to indoor air pollution. Formation of air pollution episodes is caused by the harmful emission or excessive quantities of air pollutants combined with a lack of atmospheric dispersion (Florentina and Io, 2011;Watson et al., 1988a). This association between air pollution exposure and the risks to human health has been a public health concern for over 700 years (Yin et al., 2020). Despite this early recognition of the health risks associated with poor air quality it has only become a global topic over the last 80 years [START_REF] Manisalidis | Environmental and Health Impacts of Air Pollution: A Review[END_REF]. Issues related to air quality has emerged in the human conscious with severe events like such as the Los Angeles smog in 1949, the Great London fog in 1952, and the Beijing haze in 2013 (Huang, 2018;[START_REF] Li | When London Hit the Headlines: Historical Analogy and the Chinese Media Discourse on Air Pollution[END_REF][START_REF] Mcneill | The Great Acceleration: An Environmental History of the Anthropocene Since[END_REF]Xiao, 2015). Chronic exposure also remaining risks, with air pollution leading to premature death of about 3 million population per year over the world (Anderson et al., 2012;Fang et al., 2013). In Europe, aerosols are assessed as the most worrying atmospheric pollutants along with nitrogen oxides and ozone (-Consolidated Annual Activity

Report 2018 (CAAR) -EEA annual report -European Environment Agency,‖ n.d.). Health effects include difficulty in breathing, wheezing, coughing, asthma and worsening of existing respiratory and cardiac conditions (Kim et al., 2013;[START_REF] Levy | International Primary Care Respiratory Group (IPCRG) Guidelines: Diagnosis of respiratory diseases in primary care[END_REF]. These effects result in increased medication use and premature death (Liu et al., 2018). Some studies are also showing air pollution impacts on fertility, pregnancy, premature birth, dementia, and/or obesity [START_REF] Koman | Examining Joint Effects of Air Pollution Exposure and Social Determinants of Health in Defining -At-Risk‖ Populations Under the Clean Air Act: Susceptibility of Pregnant Women to Hypertensive Disorders of Pregnancy[END_REF]Olsson et al., 2013).

The various biological mechanisms beyond pollutants health effects still remains uncertainties (Samet and Krewski, 2007), but it also has been generally recognized that particle size and chemical composition are major factors determining their health effects [START_REF] Lighty | Combustion Aerosols: Factors Governing Their Size and Composition and Implications to[END_REF]Natusch and Wallace, 1974). Exposure to fine particulate matter has largest health risk (Bell et al., 2010;Power et al., 2015), since those small particles could join into the deepest regions of the lungs and transport the toxic compounds into the bloodstream. For instance, Quan et al (2010) indicated that PM2.5 was responsible for plaque exacerbation, causing vascular inflammation and atherosclerosis. (Feng et al., 2016) reported that PM2.5 promotes the initiation and progression of diabetes and driving adverse birth outcomes. Recognizing the dangers of air pollutants to public health, the European Commission has proposed to fix the ceiling of the annual average concentration of 25μg/m 3 for PM2.5. The European Union

Chapter I 8/191 member states shall comply with this value since 2015 and the is reflecting to adopt more stringent standards in line with WHO guidelines. Furthermore, some compounds such as black carbon are suspected to be carcinogenic which have been recently classified in the 2A

(probably carcinogenic to human) and 2B (possibly carcinogenic to human) groups by IARC (International Agency for Research on Cancer) (Baan, 2007;Lauby-Secretan et al., 2016). Air pollution is one of the greatest social and environmental problems, with terrible consequences (Koolen and Rothenberg, 2019a). Therefore, an efficient air quality simulation and forecast model especially in urban region can be a key tool to assess health risks for humans exposed to airborne pollutants.

The development of atmospheric models

Atmospheric models are mathematical representations based on a complete set of dynamic equations that can generate physical and numerical data of climate and chemical parameters [START_REF] Li | Chapter 17 -Utilizing Cloud Computing to Support Scalable Atmospheric Modeling: A Case Study of Cloud-Enabled ModelE[END_REF]. Today, an atmospheric model becomes an essential tool in a variety of atmospheric sciences applications. Early in 1904, the Norwegian scientist Vilhelm Bjerknes listed seven basic variables (temperature; pressure; air density; humidity; the three components of wind velocity) and set down a two-step scientific viewpoint for the weather prediction: (i) the initial state of the atmosphere is determinates by observation giving the distribution of the variables at different levels in the diagnostic step first, (ii) then the changes over time calculated using the law of motion in the prognostic step (Lynch, 2008). By about 1922, the British scientist Lewis Fry Richardson presaged the numerical weather prediction after the advent of electronic computers in his book Weather Prediction by Numerical Process [START_REF] Inness | Operational Weather Forecasting[END_REF]Somerville, 2011). In those days, the weather forecasting was a haphazard process, the forecaster used rough techniques of extrapolation based on their knowledge of local climatology and intuition, but the principles of theoretical physics played little role in practical forecasting, weather forecasting was more an art than a science [START_REF] Lynch | Weather forecasting From woolly art to solid science[END_REF](Lynch, , 2008)). Since the 1960s, with the development of computers and network communication techniques, the atmospheric model arises at the historic moment [START_REF] Hersbach | ERA-20CM: a twentieth-century atmospheric model ensemble[END_REF]Paine, 2019). For the past half century, with the rapid development of high-performance computers, satellite detection and the continuous in-depth research of numerical prediction theory, numerical prediction has achieved great success and has become an important symbol of the atmospheric science and the main methods of weather forecasting operations (Meng et al., 2019). Although the processes of climate change are of global proportions, there are significant differences in its specific manifestations between regions (temperature; precipitation and land-use changes). Thus, atmospheric mesoscale models are being embedded to provide specific guidance to policy makers at all administrative levels.

The development of mesoscale models goes back to the 1960's, in which with a grid size of tens of kilometers to simulate regional weather over a few days (Dudhia, 2014). With the evolution of dynamics and physical representations and the availability of cheaper computing resources, high resolution simulation and forecast became possible and various -local customized‖ models began to emerge. For example, a mesoscale meteorological model coupled with a chemical transport model (CTM) is an essential tool to provide the regional airshed information to help government develop strategies for manage regional air quality. In the past two decades, those models have been used to provide information for integrated models like GAINS (Greenhouse gas -Air pollution Interactions and Synergies) for key negotiations on the air pollution control agreements [START_REF] Amann | Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications[END_REF]. New approaches based on statistics are in use or under development like the Screening for High Emission Reduction Potential on Air (SHERPA) tool. In SHERPA (Pisoni et al., 2017;Thunis et al., 2016a), a different approach is undertaken that reproduces the grid-cell-to-grid-cell approach but does not require anywhere near as many CTM runs. SHERPA assumes that the unknown parameters vary on a cell-by-cell basis but are no longer independent of each other.

According to the model design concepts and parameters, the development of CTM is roughly divided into three generations (Casado, 2013). The first generation of CTM originated in the 1970's which mainly include the box model based on the law of conservation of mass (Seinfeld, 1988) such as Empirical Kinetic Modeling Approach (EKMA), the Gaussian dispersion model based on the statistical theory of turbulence diffusion (Atkinson et al., 1997) like Industrial Source Complex model (ISC). These models use simple, parameterized linear mechanisms to describe complex atmospheric physical processes, which are suitable for simulating the long-term average concentration of inert pollutants (Watson et al., 1988b). In Chapter I 10/191 the early 1980s, with the study of the turbulence characteristics of the atmospheric boundary layer, researchers found that the Gaussian model could not answer many questions, which gradually promoted the development of the second generation. The second generation of CTM considers nonlinear response mechanism and incorporates the treatment of gas and aqueous phase chemistry. These models divide the simulated domain into many three-dimensional grid cells, the cloud, fog and precipitation scavenging processes are calculated in each cell (Carey Jang et al., 1995;Liu et al., 1984;Stockwell et al., 1990).

Representative models in this category include the Urban Air shed Model (UAM), the Regional Acid Deposition Model (RADM), etc. The second generation CTM are only designed to address individual pollutant issues such as ozone and acid deposition, which does not fully consider the mutual transformation and mutual influence of various pollutants (Reis et al., 2005). However, the physical and chemical reaction processes among various pollutants are complex in the real atmosphere. In 1990's, the US Environmental Protection Agency The WRF-Chem is a regional atmospheric dynamic-chemical coupling model developed by the US National Center for Atmospheric Research (NCAR), which is integrated with the atmospheric chemistry module in the mesoscale Weather Research and Forecast model (WRF) [START_REF] Grell | Fully coupled -online‖ chemistry in the WRF model[END_REF]. The WRF provides online large airflow fields for CTM, simulating pollutant transportation, dry and wet deposition, gas phase chemistry, aerosol formation, radiation, biological radiation, etc. [START_REF] Meng | Characteristics of trace gaseous pollutants at a regional background station in Northern China[END_REF]. The advantage of WRF-Chem is that the meteorology mode and the chemical transport mode are fully coupled in time and space resolution to achieve true online feedback. ) together constitute the Models-3 platform, of which CMAQ is the core of the entire system [START_REF] Hanna | Modelin the Transport and Chemical Evolution of Onshore and Offshore Emissions and Their Impact on Local and Regional Air Quality Using a Variable-Grid-Resolution Air Quality Model[END_REF]. The CMAQ was originally designed to comprehensively deal with complex air pollution problems such as tropospheric ozone, acid deposition, and visibility.

For this reason, the design concept of CMAQ can systematically simulate various scales and various complicated air pollution issues. The CMAQ model has become a quasi-regulatory model used by the US EPA in environmental planning, management and decision-making.

The characteristics of this model are: simultaneously simulate the behavior of a variety of air pollutants, including ozone, PM, acid deposition, and visibility and other air quality problems in different spatial scales; make full use of the latest Computer hardware and software technologies, such as high-performance computing, modular design, visualization technology, etc., make air quality simulation technology more efficient and accurate, and the application fields tend to be diversified. Since CMAQ 5.0, the model has realized the on-line coupling of meteorological model, absorbing the advantages of WRF-CHEM model [START_REF] Kong | Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII phase-2[END_REF].

CHIMERE is a three-dimensional CTM driven by meteorological drivers like MM5 or WRF.

More than 80 kinds of species more than 300 reactions are described in the model. Processes include chemistry, transportation, vertical diffusion, photochemistry, dry deposition, absorption in and below clouds, and SO 2 oxidation. Clouds are included in the process model.

It can simulate processes including gas-phase chemistry, vertical diffusion, photochemistry, aerosol formation, deposition and transport at regional and urban scales (Bessagnet et al., 2004;Vautard et al., 2005). A new version CHIMERE-V2019 has realized the online coupling of meteorological model like WRF-Chem and CMAQ (Bessagnet et al., 2020).

Compare to the first and second generation CTM, the third generation CTM has distinct advances in:

(i) full consideration of various atmospheric physical processes, chemical reactions between pollutants and gas-solid two-phase transformation process

Urbanization and air quality

In recent decades, air pollution in urban areas, especially megacities, have aroused people's attention (Baklanov et al., 2016b). With the accelerating process of industrialization and urbanization, an increasing number of people will be affected by such process especially in developing regions (Beirle et al., 2011;Fang et al., 2015;[START_REF] Hopke | Urban air quality in the Asian region[END_REF]. Urbanization was positively related to global health in the short term and long term. In the short run, 1%

increase in urbanization was associated with reduced mortality, under-five mortality, and infant mortality of 0.05%, 0.04%, and 0.04%, respectively, as well as increased life expectancy of 0.01 year [START_REF] Wang | Urbanization and Global Health: The Role of Air Pollution[END_REF]. However, the process of urbanization has also made important contributions to regional climate changes and has caused a bad effect on ecosystem and extreme climate significantly increased (Changnon et al., 1996;Semenza et al., 1996).

Since the end of World War II, the world's population has grown from 2.5 billion to about 7 billion today, the urban pollution has risen from below 30% to 58% in the past seventy years.

Air pollution in cities is exacerbated by increased human activity in urban areas due to population growth (Miranda et al., 2015). The recent global lockdown events response to COVID-19 pandemic have reduced the NO 2 concentrations and particulate matter levels by about 60% and 31% respectively by using satellite data and a network of more than 10000 air quality observation stations in 34 countries (Venter et al., 2020), the NO 2 and PM2.5 concentration in China was reduced by around 20% and 17% for 30-50 days (Z. [START_REF] Liu | COVID-19 causes record decline in global CO2 emissions[END_REF]Zinke, 2020). Using the mesoscale meteorological and chemical transport model, Menut et al.(2020) noted a decrease of -30% to -50% for NO 2 and -5% to -15% for particulate matter in all western Europe counties during the lockdown events. In general, air pollution is positively correlated with the initial and acceleration stages of urbanization, with pollution in major cities tending to increase during the construction phase, passing through a maximum pollution level and then again decreased at the end of the urbanization process as abatement strategies are developed (Fenger, 1999). In the industrialized Europe countries, urban air pollution is in some respects in the last stage with effectively reduced levels of many pollutants such as Sulphur dioxide, carbon monoxide and lead (Fenger, 2002). Figure1.5

shows that ground-level concentrations of almost all pollutants have declined steadily since 2000 in 28 countries Europe Union (EU28). The two notable results concerning emission reductions are the total SO x from the EU28 member states were cut by 69 % since 2000 and the almost no changes of total ammonia (Koolen and Rothenberg, 2019).

Figure1.5 Emission trends in the EU28 on the basis of fuel sold (Koolen and Rothenberg, 2019) Besides, geographical condition also plays an important role in transportation and dispersion of pollutants. Pollutants dispersion processes over the valley-basin city are much more complicated than over flat areas. Therefore, pollution episodes have been frequently

witnessed over complex terrain, especially in wintertime (Chen et al., 2019;Sabatier et al., 2020). In coastal cities, shipping emissions contribute to air quality degradation. 2016). For example, computational fluid dynamics studies indicated that the flow resistance of tree crowns decreased wind speed and the dispersion of pollutants is limited in the ground level and the particulate matter concentrations accumulate within the canyon [START_REF] Gromke | Influence of avenue-trees on air quality at the urban neighborhood scale. Part II: Traffic pollutant concentrations at pedestrian level[END_REF]. These diversified types of artificial areas appear alternately, leading to complex, diverse, and non-uniform urban canopy characteristics, which give more complex turbulent airflows in urban region, increased the difficulty of air quality simulation and forecast in cities than in other regions. The urban canopy, urban street canopy and urban boundary layer consist of the urban micro environment. The boundary layer is the atmospheric structure closest to the underlying surface. The meteorological conditions within the boundary layer play an important role in the formation of air pollution (Bianco et al., 2008). The wind field determines the regional transportation of pollutants. The height of the atmospheric boundary layer determines the amount of ventilation and the dilution capacity of pollutants. Heavy pollution episodes are always accompanied by a low boundary layer height (Quan et al., 2014;Yin et al., 2019). In cities, urbanization causes thermodynamic perturbations and facilitates the development of the boundary layer (Miao et al., 2019).

Moreover, some aerosols like black carbon can warm the upper PBL which help to stabilize the boundary layer height and weaken turbulent mixing, resulting in a decrease of the boundary layer height (BLH), which enhances the accumulation of air pollutants (Ding et al., 2016;Li et al., 2017).

Overall, cities are highly sensitive to the impact of meteorological disasters. How to be efficient in extreme climates risk analysis and early warning, firmly keeping the bottom line of city safe operation and ecological environment protection is a concern of scientists and politicians. The impact of the accelerated urbanization process on the regional climate and atmospheric environment mainly includes the following aspects:

(1) The urban underlying surface is defined as the part of the city in direct exchange with the atmosphere (Y. [START_REF] Li | Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984-2018[END_REF], the changes of the urban underlying surface have changed the environmental properties of the natural underlying surface;

Chapter I 16/191 [START_REF] Yao | The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> Chapter V 159/191 directly emitted from vehicles[END_REF] The urban canopy has a "shading effect" on solar radiation, which reduces the solar radiation reaching the surface. At the same time, there is the exchange and storage of radiation energy between the buildings in the canopy, which affects the surface energy balance;

(3) Human activities will increase atmospheric pollutants and artificial heat generation. In short, in addition to bringing economic and social benefits to humanity, urbanization has also brought many problems to the urban climate and atmospheric environment

Statement.

The climate effect brought by urbanization and its impact on the atmospheric environment is becoming one of the key issues of the world.

Figure1.6 Urban heat island in cities.

Objectives and outline of the PhD thesis

The main goal of this PhD work is to improve the air quality simulation and forecast in France especially in urbanized areas. For its operational requirements, the system requires continuous improvements, particularly the ability to better predict certain types of episodes by stable and calm meteorological situations and these latter conditions are difficulty captured by meteorological models.

Different geographical areas will be investigated in France, especially mainly for short-term pollution episodes. The ability of models to assess air quality management strategies is also important, it will be appropriate during this study to perform the diffusion and dispersion of main pollutants and possibly improve the vertical diffusion coefficient in the selected CTM (CHIMERE here) (Menut et al., 2013;Mailler et al., 2017).

The objectives of this work are to provide clues and answers to the following questions:

1) Which formulae for computing the vertical diffusion coefficient based on K-theory gives the best performances on air quality simulation?

2) How well do physics parameterizations impact the urban air quality simulation and forecast?

3) How does vertical grid resolution and first layer height impact the meteorological and chemistry transport modelling?

4) Can we improve the vertical mixing through the use of turbulence kinetic energy sub-grid eddy coefficient?

To tackle these issues, the present work has been breakdown in four main steps detailed in the various chapters.

Chapter II addresses the results of the one-year air quality simulation that was determined by three K-theory vertical diffusion coefficient. Inter-annual trends, seasonal and daily variations of main pollutants concentrations are presented. Here, it has been chosen to focus both on cities and surroundings over the whole France.

In Chapter III, the impact of physics parameterizations on high-resolution meteorological and air quality simulations over the urban region will be analyzed, with a focus on a winter pollution episode in Paris region. Three canopy schemes, three boundary layer schemes, two land surface schemes will be tested in this part. Benefits as well as drawbacks and specific issues of the considered approaches are discussed and compared. Finally, the most reasonable physics parameterizations will be used in the subsequent chapters.

Chapter I

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In Chapter IV, the impact of vertical gird resolution and first layer height on meteorological and chemistry transport modelling will be analyzed; three vertical resolution configurations and three first layer height will be applied in a fifteen days winter episode including a pollution event.

In Chapter V, we use a 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme from WRF defined as NED on the urban air quality context to assess consequences of surface-level pollutants concentrations in Paris, Lyon and Bordeaux regions.

General principle of the chemical transport model

The chemical transport model (CTM) is a mathematical tool used for air quality research. It uses numerical methods to describe the dispersion, diffusion, chemical reactions and removal process of pollutants in the atmosphere (Karl, 2018). Emission sources, topography and meteorological data of the studied area are the key elements for an air quality simulation. In other words, the tool used for air quality simulation and forecast not only the CTM itself, but a simulation system with CTM as the core, including multiple related components. An air quality simulation system usually consists of five parts: meteorological field, pollutant emissions, boundary conditions, post-analysis procedures and the CTM core [START_REF] Sarath | Air Quality Forecasting System for Cities: Modeling Architecture for Delhi[END_REF]. The framework of the system is shown in Figure2.1.

Figure2.1 General principle of the air quality simulation system

For a limited-area simulation, the model cannot solve for points on the edge of the grid because there are no points beyond the boundary to use for evaluating the derivative perpendicular to it (Warner, 2011). Therefore, the lateral-boundary condition need specified for the value of dependent variables at these boundary points. In addition, the upper and lower boundary conditions also must be specified in mesoscale model. For urban and regional scale numerical simulations, the usual method for determining boundary conditions is to use the results of larger-scale regional models or global-scale models as boundary conditions. The meteorological field provides the meteorological data with a certain temporal and spatial resolution, such as wind field, temperature, mixed layer height, radiation, vertical diffusion coefficient, cloud characteristics, etc. (Rao et al., 2003). Usually, in air quality models, the atmospheric motion control equation is independent of the pollutant chemical dynamics equation. Thus, the formation of pollutants in the atmosphere will not significantly change the meteorological conditions and the simulation of meteorological field is independent of air quality simulation, this case is called as offline approach (Baklanov et al., 2016a;Seo et al., 2018). However, changes of pollutants concentrations have a non-negligible impact on meteorological conditions in some cases, in this case online approach is required. Study on the coupling of mesoscale meteorological models and CTM have shown that changes in pollutant concentration have slight impact on meteorological conditions (Bessagnet et al., 2020;[START_REF] Jacobson | Development and application of a new air pollution modeling system-II. Aerosol module structure and design[END_REF]. Therefore, most air quality simulation systems use offline approach.

Also, an identified drawback regarding the boundary conditions is a possible inconsistency of the model species and chemical regimes between the large-scale model and the nested regional model if they are different.

Accurate emission inventories play a crucial role in the simulation results. The list of species should satisfy the chemical reaction mechanism and temporal and spatial resolution of the model. For Eulerian models, gridded hourly emission data is usually required. The main pollutants include CO; NO x ; SO 2 ; NH 3 ; PM; VOCs; etc (Crippa et al., 2018). For practical applications to support decisions and policy making, the purpose of air quality modeling is to grasp the characteristics and laws of pollution, explore its causes, and finally provide scientific control countermeasures [START_REF] Zhang | Research on the Topological Properties of Air Quality Index Based on a Complex Network[END_REF]. The post-analysis program uses the observational data of the ground observation stations to analyze the simulation results which provide information toassess the impact on the environment and the economy and formulate corresponding control plans. In general, the principle of CTM should following those steps: (i)

understand and determine the physical processes are needed for the simulation; (ii) choose a sufficiently small horizontal grid increment to resolve all the processes to be simulated on the grid, numerically a finer resolution is supposed to be better; (iii) select the most suitable projection map for the range of the model grid, (iv) ensure the vertical resolution is reasonably compatible with the horizontal resolution, and can accurately define important vertical structures (Warner, 2011).

Urban meteorology

In the past centuries, humans are increasingly gathering in large settlements, as well as the urban population exceeded the rural [START_REF] Morais | Implementation of Observed Sky-View Factor in a Mesoscale Model for Sensitivity Studies of the Urban Meteorology[END_REF]. The population is concentrated in less than 3% of the Earth's land area [START_REF] Baklanov | From urban meteorology, climate and environment research to integrated city services[END_REF]. It is expected that two-thirds of the world's population will live in cities by 2050 (WUP 2014), therefore general knowledge of climatic characteristics of typical cities and differences with rural areas are becoming more and more crucial (Karlický et al., 2020).

Urbanization has a major impact on urban meteorology. It can change the atmospheric diffusion capacity thereby affecting the concentration of pollutants (Liu et al., 2015), weather and climate at local, regional and global scales (Baklanov et al., 2007). First, the urban surface are mostly characterized by buildings, roads and permeable natural surfaces, where various physical processes such as radiation trapping, atmospheric flow, turbulent heat exchange, and underground heat conduction occur, resulting in a unique local meteorology [START_REF] Kim | Development of a Building-Scale Meteorological Prediction System Including a Realistic Surface Heating[END_REF][START_REF] Uehara | Wind tunnel experiments on how thermal stratification affects flow in and above urban street canyons[END_REF]. Second, changes in chemical emissions and feedbacks of atmospheric pollutants affect the weather and climate both locally and further afield (Reinmuth-Selzle et al., 2017). The main examples includes: (i) the distribution of rough elements which can affect the turbulence state, speed and flow direction; (ii) the extensive use of impervious materials which generally reduces vegetation coverage in urban areas thereby affecting the hydrological and meteorological conditions and the deposition of pollutants; (iii) the urban canopy affecting the flow and heat exchange between different land covers; (iv) the release and diffusion of pollutants can affects radiation transmission and the formation of clouds and precipitation [START_REF] Baklanov | From urban meteorology, climate and environment research to integrated city services[END_REF]. The most well-known urban meteorology is the so-called urban heat island (UHI), which was first described by [START_REF] Oke | Urban heat island dynamics in Montreal and Vancouver[END_REF] in the year 1975 and has been extensively studied in the following decades (Baklanov et al., 2016c;Karlický et al., 2020;[START_REF] Kim | [END_REF][START_REF] Oke | The energetic basis of the urban heat island, Q[END_REF]Zhang et al., 2017). In short, it can be described as urban temperature is higher than in the countryside.

UHI not only affects the microscale and mesoscale circulation to a large extent, but also cause the formation of convection (Dixon et al., 2003;). Several studies have demonstrated that the increase in the number of convection phenomena in cities were related to the increase in the convergence of the lower-level over urban areas [START_REF] Fujibe | Long-term Surface Wind Changes in the Tokyo Metropolitan Area in the Afternoon of Sunny Days in the Warm Season[END_REF]. Theeuwes et al. (2019) found the cloud cover enhancement over urban region of Paris and London during summer because of increased convection caused by UHI. With the rapid improvement of computer capabilities and the widespread application of remote sensing technology in meteorology, geographic information on urban surfaces can be obtained with higher precision, which makes it possible to develop and apply high-resolution urban boundary layer models. Some studies indicate that meteorological variables are significantly affected by the urban canopy. For example, the impact of urban boundary layer structure causes changes in dispersion conditions (Karlický et al., 2018), which have considerable consequences for primary or secondary pollutant concentrations (Huszar et al., 2020;Li et al., 2019). Manola et al. (2020) found the overall precipitation increased in the urban areas of Amsterdam in comparison to rural surroundings.

In general, the relationship between meteorology and city is mutual: meteorology affects how urban space is used such as the different concerns between coastal cities and landlocked cities (Pelling and Blackburn, 2013) or tropical cities and boreal cities. In turn, the city has changed the regional meteorology conditions, resulting in differences in cloud cover, solar radiation, temperature and wind speed between the city and its surrounding areas thereby resulting the transport, diffusion, and chemical transformation of particulates and gases. Figure2.2

summarized the main linkages between cities, air quality and climate. Specifically: (i) non-linear interaction and its feedback between emissions, land use, reaction and meteorology;

(ii) complex spatial scales from local to global and time scales from minutes to centuries; (iii) mixing of pollutants from multiple pollution sources; (iv) interaction between city characteristics and emissions (Baklanov et al., 2010).

Figure2. [START_REF] Yao | The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> Chapter V 159/191 directly emitted from vehicles[END_REF] The main linkages between megacities, air quality and climate (Baklanov et al., 2010)

Urban canopy modeling

There are numbers of methods used to represent the dynamic and thermodynamic effects of urban areas on the atmosphere and each city has its unique spatial structure and urban metabolism characteristics [START_REF] Ching | A perspective on urban canopy layer modeling for weather, climate and air quality applications[END_REF]. The smartest solution is to use a computational fluid dynamics (CFD) model, which clearly expresses the influence of each building on the atmospheric structure (Kirkil et al., 2012;Letzel et al., 2008). However, those fine-precision CFD models are extremely computationally luxurious to use. Based on the comprehensive consideration of model performance and computing resources, mesoscale meteorological and CTM models are increasingly being applied up to urban areas (Burian et al., 2007) with fine grids up to 1km resolutions [START_REF] Tong | Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong[END_REF].

In contrast to the fine-precision CFD models, for meteorological and air quality simulations in urban areas, all the normal mesoscale needed is the overall contribution of the bulk effects of built-up areas on the mesoscale processes. The mesoscale model defines surface properties through adjusting parameter attributes associated with land use and other surface covers datasets to represent the altered morphological, reflective, and anthropogenic heating aspects so that they approximate the artificial surface conditions in cities (Beltran-Przekurat et al., 2012). For example, the roughness length can be used to indicate the resistance of the building. The albedo can be reduced to account for the existence of dark roofs and shortwave radiation capture in street canyons. The heat capacity and thermal conductivity of the substrate can be increased to solve the problem of heat storage in the walls of buildings;

reduce the water capacity of the substrate to reflect the universality of impervious surfaces (Liu et al., 2006). The six key physical parameters of land-use categories are albedo, emissivity, surface thermal inertia, roughness length, soil moisture availability and soil heat capacity, respectively (H. [START_REF] Li | Transformation of Urban Surfaces and Heat Islands in Nanjing during 1984-2018[END_REF]. However, given the complexity of the urban system, those similarity frameworks may encounter problems when applied in the urban context [START_REF] Roth | Review of atmospheric turbulence over cities[END_REF], some other schemes are needed to represent more complex processes related to the presence of buildings and non-natural substrates. Oke (1988) provided a concept of the atmospheric regimes overlying urban areas for the first time. Since the flow convection and atmospheric processes respond to the heterogeneity of the urban surface, the boundary layer structure over urban areas are necessarily multi-scale [START_REF] Klein | Driving physical mechanisms of flow and dispersion in urban canopies[END_REF]. The varying buildings heights, distribution densities and varying street widths create a mean condition in the urban canopy. The layers above urban canopy is continuously adjusted by the vertical flux caused by the horizontal changes and gradients of the underlying land use and canopy structure. This situation produces countless internal boundary layers, and the turbulence generated in each boundary layer is balanced by dissipation. Urban canopy parameterizations with varying complexities were developed and coupled with the model to improve its skills in capturing fine-scale urban processes and in assessing the environmental issues (Kondo et al., 2005;Kusaka et al., 2001;Kusaka and Kimura, 2004;Liao et al., 2014). The single layer urban canopy scheme (UCM) parameterizes the aggregate effects of the urban morphology, but does not explicitly represent the effects of individual buildings and street canyons (Kusaka et al., 2001). The UCM considered radiation capture and wind shear in urban canopy affected by the geometry of buildings and roads (Huang et al., 2019). However, the UCM is a single layer parameterization, which means moisture, heat and momentum fluxes are defined at the bottom boundary of the lowest atmosphere in the model (Warner, 2011). Thus, a multiple layer building effect parameterization scheme (BEP) was developed by (Martilli et al., 2002) in order to account for the three-dimensional nature of urban surfaces and the fact that buildings distribute sources and heat, moisture and momentum vertically throughout the urban canopy. The impact of urban canopy schemes will be discussed in the Chapter III of the manuscript.

The basic equations and dispersion in the urban canopy layer

In the atmospheric context, anthropogenic emissions are mainly influential on the lower urban atmosphere layers and communicated to regional, global, and smaller scales via transport and turbulence processes [START_REF] Fernando | Flow, turbulence, and pollutant dispersion in urban atmospheres[END_REF]. The understanding of the wind flows in an urban region and the concomitant dispersion of material released in that flow is gaining importance.

Britter [START_REF] Britter | Flow and dispersion in urban areas[END_REF] suggested that the dispersion in urban area should be classified in four scales of domain coverages: (i) 100km to 200km defined as regional scale; (ii) 10km to 20km defined as city scale; (iii) 1km to 2km defined as neighborhood scale; (iv) 100m to 200m defined as building scale.

Although there are a large diversity of Eulerian models, most of them are built on the same pollutant continuity equation as (4.1): Figure2.3 Example of mass conservation (Jacobson, 2005).

( ) ( ) ( ) ( ) ( ) ( ) (
However, because atmospheric motion is a turbulent motion, that is, irregular motion.

Therefore, it is difficult to characterize the transport flux density F = uρ. Especially in the vertical direction, the turbulent motion causes the gas concentration and vertical wind speed to fluctuate rapidly over time.

In the simulation processes, what is interesting is not the instantaneous flux value, but the average flux over a certain period. Therefore, Reynolds decomposition can be used to deal with the problems caused by turbulence. Decompose the value of ρ and u, v, w into two parts:

the average value and the disturbance value relative to the average value as the equation (1.2):

( ) '( ) ( ) '( ) ( ) '( ) ( ) '( ) (1.2)
With , , , the average value of ρ, u, v, w in a certain period of time; ρ'(t), u'(t), v'(t), w'(t) the disturbance value relative to the average value. According to the definition (1.3):

' 0, ' 0 , ' 0, ' 0 (1.3)

In x, y, z directions (1.4):

Chapter II 40/191 ' ' ' ' ' '

(1.4)

With the advection flux, produced by the transport of average wind; the turbulent flux, produced by the covariance of wind speed and concentration. In the vertical direction, the upward and downward motions caused by turbulence are generally close, the average wind speed often smaller than the disturbance value. This causes to be usually greater than , which is the main part that affects the vertical transport. In the horizontal direction, it is the opposite (Jacobson, 2005).

In CTMs, empirical parameterization methods are required to estimate the turbulent fluxes [START_REF] Dwivedi | Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods[END_REF]. One of the widely used methods is so called K-theory. Assuming that the diffusion process caused by turbulent motion is similar to molecular diffusion, analogy to the law of molecular diffusion defines an empirical diffusion coefficient K, as the equation (1.5): ' ' ' ' ' '

(1.5) With , , the diffusion coefficient in x, y, z direction, respectively. Such scheme only retains the prognostic equations for the zero order mean variables like temperature, wind speed and humidity, which is also called first order local closure scheme [START_REF] Stull | Turbulence Kinetic Energy, Stability and Scaling[END_REF]. This scheme works well when the turbulent eddies are relatively small and locally generated.

This chapter addresses the results of the one-year air quality simulation that was performed with three different first order local closure scheme (K-theory scheme) to estimate the vertical diffusion coefficient (Kz) within the boundary layer The initial Kz in CHIMERE is defined as the equation (1.6):

( ) (1.6)
where k is the von Karman constant set to 0.41 in the model, z is the altitude, h is the boundary layer height, and w s is the vertical scale given by the similarity formulae.

Convective turbulence driven by buoyancy dominates during the day, while turbulence driven by wind shear at night is more dominant (Warner, 2011). Figure2.4 shows the typical diurnal variation of the boundary layer structure. The boundary layer height during the day is determined by vertical extent of the turbulent mixing. During the daytime, sunshine increases the surface temperature, buoyancy generates turbulence and extends to the troposphere, and the convective mixing layer increases. Usually the thickness is about 1000m. This period is called an unstable condition in the vertical mixing (Troen and Mahrt, 1986). At night, as the temperature decreases, the buoyancy gradually disappears. The energy of the turbulence near the surface can only calculated from the vertical wind shear of the horizontal wind. Therefore, in most cases, the boundary layer is much higher during the day than at night, which is so-called stable condition in mixing process.

Figure2.4 Typical diurnal variation of the boundary layer structure [START_REF] Oke | The energetic basis of the urban heat island, Q[END_REF]. Sunrise (SR), Sunset (SS).

In stable conditions (h/L ≥ 0), the w s is defined as (1.7):

⁄ (1.7)
In unstable conditions (h/L < 0), the w s is defined as (1.8):

( ) (1.8)
where e = max (0.1, z/h). The Monin-Obukhov similarity theory widely used in mesoscale CTM model and it has many expressions. For the k-theory in the Community Multiscale Air Quality Model (CMAQ), the Kz is defined by [START_REF] Holtslag | Local Versus Nonlocal Boundary-Layer Diffusion in a Global Climate Model[END_REF] as (1.9):

( ) (1.9)
In stable conditions, the w s is defined as (1.10):

(1.10)

In unstable conditions, the w s is defined as (1.11):

( ) (1.11)
In a three-dimensional Eulerian model (FALL3D), Kz is defined as [START_REF] Costa | A three-dimensional Eulerian model for transport and deposition of volcanic ashes[END_REF] (1.12):

( ) (1.12)
In stable conditions, the w s is defined as (1.13):

( ) (1.13)
In unstable conditions, the w s is defined as (4.1):

( ) (1.14)
All three schemes use the friction velocity (u * ), convective velocity (w * ) and the Monin-Obukhov length (L) but in different configurations for stable and unstable conditions.

Preliminary study -Model setup

As a first analysis, a long-term simulation covering the entire year 2016 has been performed

with three Kz parameterizations as previously mentioned. The air quality monitoring data are taken from the French national air quality forecasting platform (PREV'AIR) [START_REF] Honore | PREV'AIR, a platform for air quality monitoring and forecasting[END_REF]. 57 urban background air quality monitoring stations and 15 rural background air quality monitoring stations includes the hourly surface concentrations of criteria pollutants (NO 2 , PM2.5, and PM10) are used to assess the performances of the models with the various Kz parameterizations, the locations of stations is shown in Figure2.5.

Figure2.5 Locations of air quality monitoring stations. Red points: urban background stations. Green points: rural background stations

To quantify the difference between model and instruments data, the relative bias (RB), mean bias (MB), mean linear correlation coefficient (R) and root mean square error (RMSE) is used as statistical estimation and calculated as:

( ) (1. 15) (1. 16) ∑( ̅̅̅̅̅̅̅) ( ̅̅̅̅̅̅) √∑( ̅̅̅̅̅̅̅) ∑( ̅̅̅̅̅̅) (1. 17) √ ∑( ) (1. 18)
Where refers to the modelled value and refers to the results from instruments with ̅̅̅̅̅̅̅and ̅̅̅̅̅̅ their mean values respectively. N is the number of the data.

Results and Discussions

A sensitivity test using the same model configurations, but different vertical diffusion coefficients was performed to understand the role of each Kz in stable and unstable conditions.

As shown in Figure2.6, three schemes display similar Kz at low levels in stable condition, with a relatively high surface diffusion rate in Kz3 and relatively low surface diffusion rate in Kz2, the Kz1 shows a slightly higher Kz in all levels. However, Kz3 has obviously stronger Kz since the fifth level than other two schemes. Under unstable conditions, the surface Kz shows a completely opposite trend to stable condition, with Kz2 > Kz1 > Kz3. In the middle levels, the difference between the different schemes is significantly higher than the stable condition, and the diffusion rates of Kz2 and Kz3 begin to reverse in the ninth level. According to this sensitivity test, three conclusions can be drawn: the differences between each scheme is small; the RB of pollutants concentrations from Kz3 will be higher than other two; and the RB of pollutants concentrations between three schemes will be higher in the nighttime than the daytime.

Figure2.6 Sensitivity test of vertical profile of Kz (m 2 /s) in stable and unstable conditions. Unstable condition 2016-11-20 1200UTC Figure2.7 Vertical profiles of Kz (m 2 /s); NO 2 (µg/m 3 ); O 3 (µg/m 3 ) PM2.5 (µg/m 3 ) and PM10 (µg/m 3 ) over the France domain. The profiles are averaged from January 1 st to December 31 st , 2016.

Table2.1 The Kz (m 2 /s) values for the 10 levels. Figure2.8 displays the seasonal mean first layer Kz over France domain. Results show that the regional patterns are similar for three schemes. The Kz1 shows a slightly higher surface Kz than Kz3 at the Alps and the Pyrenees. However, Kz3 gives a relative higher surface Kz than Kz1 over mainland France in autumn and winter. In general, Kz2 scheme has slightly higher vertical diffusion rate than other two schemes, the three schemes give the trend of spring > summer > autumn > winter of surface Kz in the France domain, and coastal areas are generally display higher Kz than inland areas, due to the sea breezes which enhance the dispersion of pollutants [START_REF] Reid | Diurnal variations of boundary-layer carbon dioxide in a coastal city-Observations and comparison with model results[END_REF]. The seasonal profiles of surface NO 2 , PM2.5 and PM10 concentrations between observation and simulation are illustrated in Figure 2.9.

Model underestimated NO 2 concentrations whatever the season, but particularly during wintertime. The negative biases are -6.05µg/m 3 , -6.16µg/m 3 and -7.09µg/m 3 for Kz1, Kz2 and Figure2.8 Maps of the seasonal mean vertical diffusion coefficient (m 2 /s) over the France domain.

Figure2.9 Seasonal changes between observed and modelled NO 2 , PM2.5 and PM10 mass concentrations (µg/m 3 ) in France domain.

Table2.2 Seasonal average mass concentration of NO 2 , PM2.5 and PM10 (µg/m 3 ) between observation and three schemes. In order to better evaluate the impact of vertical diffusion coefficients on the air quality predictions, modelled time series of the pollutants concentrations compared with the observations for urban and rural region are displayed in Figure2.10. In urban areas, the models capture the time variability quite well for NO 2 , PM2.5 and PM10 simulations, particularly for the PM2.5, model almost perfectly reproduced the seasonal variability. ) and (1.9). The range of this value has been considered from 1 to 3 by Troen and Mahrt (Troen and Mahrt, 1986), the vertical mixing varies considerably correspond to the range of p from 1 to 3 (Hu et al., 2010). Therefore, the results show that setting p value to 2 is applicable in rural areas, but the p value is overestimated in urban areas. The easiest way to improve such a first order local closure scheme is to set p according to types of land use. Comparing the performances of three k-theory schemes and taking RMSE as the criterion for judging the ability of the model, in urban areas lead to the following outcomes on concentrations: Kz1 > Kz2 > Kz3 for NO 2 ;

Kz3 > Kz2 >Kz1 for PM2.5 and Kz1 > Kz3 > Kz2 for PM10; in rural areas, Kz1 > Kz2 > Kz3

for NO 2 , Kz3 > Kz2 >Kz1 for PM2.5 and Kz2 > Kz3 > Kz1 for PM10. However, the differences between the test results are low. Therefore, it cannot be expected to significantly improve the mesoscale model for air quality simulation and forecast through the modification of diffusion coefficient based on first order local closure scheme.

Figure2.10 Time series between observed and modelled NO 2 , PM2.5 and PM10 mass concentrations (µg/m 3 ) in Urban and Rural areas. In Figure2.11 the results from urban and rural areas are illustrated for surface NO 2 , PM2.5 and PM10 concentrations . The diurnal cycles of NO 2 , PM2.5 and PM10 have a similar behavior at urban areas, with two peaks occurred in the morning and evening rush hours. In rural areas, the evening peak did not appear, with show a gradual accumulation at nighttime. Compared with observations, the simulation results have more significant diurnal differences for all pollutants, and the model significantly underestimates the NO 2 and PM10 concentration during daytime. This indicates that the K-theory may overestimate the vertical diffusion rate under unstable conditions. The model performance between the three schemes shows higher biases in nighttime than for daytime, especially for the NO 2 simulation, which proved the conclusion in the sensitivity test that the RB between three schemes will be higher in absolute values in the nighttime than the daytime.

Table2.3 Statistics of surface variables: mean linear correlation coefficient (R); mean bias (MB) and root mean square error (RMSE) between the simulations and observations of NO

Conclusions

The micro meteorology over built-up areas differs from the non-urban landscape in many aspects: wind-flows, radiation, humidity, precipitation and air quality. The dispersion of pollutants in urban areas is an actual topic and the modelling approaches depend on the involved atmospheric processes. Initially we focused on the dispersion of pollutants in the urban canopy, three vertical diffusion coefficients based on first order local closure scheme have been investigated in a one-year simulation over France. Comparing the model performances of three scheme in urban and rural areas, it is found that the three schemes perform better in rural than in urban areas. The vertical mixing is relatively overestimated in urban areas. One idea is to decrease the p value (the -square‖ in the Kz equation) on urban land use. The results also indicate such simplest scheme is an effective way to reproduce the dispersion of pollutants both in urban canopy and surroundings in mesoscale chemistry transport model, but it cannot be expected to significantly improve the mesoscale model by this simple modification based on the first-order local closure Reynolds decomposition scheme. Further improvements should start with a more accurate turbulence process. Another problem to be solved is the ratio of PM2.5 / PM10 that exceeds 90% in all seasons in the models, while the ratio is usually around 70% in the observations, the emission inventory requires notable improvements with probably a lack of coarse particles anthropogenic or biogenic in origins. This first screening of usual K-theory diffusion parameterizations over a long-term simulation was a first step to identify limitations and possible improvements. The next chapter focuses at urban scale on pollution event in November-December 2016. Lei Jiang 1,2,,Bertrand Bessagnet 1 ,Frederik Meleux 1 ,Frederic Tognet 1 ,Florian Couvidat 1 1 INERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550 Verneuil-en-Halatte, France; 2 Sorbonne University, UPMC Univ Paris 06, ED129, 75005 Paris, France

Impact of physics parameterizations on high-resolution air quality simulations over the urban region

Abstract

The accurate simulation of meteorological conditions, especially within the planetary boundary layer (PBL), is of major importance for air quality modeling. In the present work, we have used the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region. A lower first model layer with a 4m surface layer could better reproduce the transport and diffusion of pollutants in a real urban environment. Three canopy models could better reproduce a 2m temperature (T2) in the daytime but present a positive bias from 1°C to 5°C during the nighttime; the multi-urban canopy scheme -building effect parameterization‖ (BEP) underestimates the 10m windspeed (W10) around 1.2m/s for the whole episode, indicating the city cluster plays an important role in the diffusion rate in urban areas. For the simulation of pollutant concentrations, large differences were found between three canopy schemes, but with an overall overestimation during the pollution episode, especially for NO 2 simulation, the average mean biases of NO 2 prediction during the pollution episode were 40.9 µg/m 3 , 62.2 µg/m 3 , and 29.7µg/m 3 for the Bulk, urban canopy model (UCM), and BEP schemes, respectively. Meanwhile, the vertical profile of the diffusion coefficients and pollutants indicated an important impact of the canopy model on the vertical diffusion. The PBL scheme sensitivity tests displayed an underestimation of the height of the PBL when compared with observations issued from the Lidar. The YonSei University scheme (YSU) and Boulac PBL schemes improved the PBL prediction compared with the Mellor-Yamada-Janjic (MYJ) scheme. All the sensitivity tests, except the Boulac-BEP, could not fairly reproduce the PBL height during the pollution episode. The Boulac-BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and pollutants, especially for the NO 2 and PM2.5 (particulate matter 2.5 micrometers or less in diameter) simulations. The mean bias of the NO 2 , PM2.5, and PM10 (particulate matter 10 micrometers or less in diameter) prediction were -5.1µg/m 3 , 1.2µg/m 3 , and -8.6µg/m 3 , respectively, indicating that both the canopy schemes and PBL schemes have a critical effect on air quality prediction in the urban region. The air quality simulation and forecast in urban region mainly influenced by the urban canopy, boundary layer and diffusion conditions, land surface model had a slight impact for both meteorological and air quality simulation.

Ré sumé

La simulation pré cise des conditions mé té orologiques, en particulier dans la couche limite plané taire (PBL), est d'une importance majeure pour la modé lisation de la qualité de l'air.

Dans le pré sent travail, nous avons utilisé le modè le Weather Research and Forecast (WRF) couplé au modè le de transport de la chimie (CTM) CHIMERE pour comprendre l'impact des paramé trages de la physique sur la simulation de la qualité de l'air pendant un é pisode de pollution de court terme sur la ré gion parisienne. Un premier niveau vertical du modè le a 4m permettrait de mieux reproduire le transport et la diffusion des polluants dans un environnement urbain. Trois modè les de canopé e pourraient mieux reproduire une tempé rature de 2m (T2) dans la journé e mais pré sentent un biais positif de 1°C à 5°C pendant la nuit ; le sché ma de canopé e multi-urbain "building effect parameterization" (BEP) sous-estime la vitesse du vent à 10m (W10) d'environ 1.2m/s pour l'ensemble de l'é pisode, indiquant que le cluster de la ville joue un rôle important dans la diffusion des polluants en zones urbaines. Pour la simulation des concentrations de polluants, de grandes diffé rences ont é té constaté es entre les trois sché mas de canopé e, mais avec une surestimation globale pendant l'é pisode de pollution, en particulier pour la simulation du NO 2 , les biais moyens de la pré diction du NO 2 pendant l'é pisode de pollution é taient de 40,9µg/m 3 , 62,2µg/m 3 et 29,7µg/m 3 pour les sché mas Bulk, urban canopy model (UCM) et BEP, respectivement.

Parallè lement, le profil vertical des coefficients de diffusion et des polluants indique un impact important du modè le de canopé e sur la diffusion verticale. Les tests de sensibilité du sché ma PBL ont montré une sous-estimation de la hauteur de la PBL par rapport aux observations fournies par le Lidar. Les sché mas PBL de l'Université YonSei (YSU) et Boulac ont amé lioré la pré diction de la PBL par rapport au sché ma Mellor-Yamada-Janjic (MYJ).

Tous les tests de sensibilité , à l'exception du Boulac-BEP, n'ont pas pu reproduire fidè lement la hauteur de la PBL pendant l'é pisode de pollution. Le sché ma Boulac-BEP a eu des performances significativement meilleures que les autres sché mas pour la simulation de la hauteur de la CLP et des polluants, en particulier pour les simulations de NO 2 et PM2.5 (particules de 2,5 micromè tres de diamè tre ou moins). Le biais moyen des simulations de NO 2 , PM2.5 et PM10 (particules de 10 micromè tres ou moins de diamè tre) é tait de -5,1µg/m 3 , 1,2µg/m 3 et -8,6µg/m 3 , respectivement, ce qui indique que les sché mas de canopé e urbaine et de PBL ont un effet critique sur la pré vision de la qualité de l'air dans la ré gion urbaine. La simulation et la pré vision de la qualité de l'air dans la ré gion urbaine sont principalement influencé es par la canopé e urbaine, la couche limite et les conditions de diffusion, le modè le de surface terrestre ayant un plus lé ger impact sur la simulation mé té orologique et la qualité de l'air. Keywords: modeling; high resolution; air quality; urban canopy; surface layer

Introduction

Nowadays, around 55% of the world's population lives in urban areas, and this number is expected to increase by 68% by 2050. The European Environment Agency annual report has pointed out around 25% of the European urban population are exposed to air quality exceeding the European Union air quality standards (EEA, 2018), and air pollution is the leading preventable risk factors for premature death in Europe, being responsible for 400,000 deaths per year directly or indirectly. In addition, built-up surfaces that are mainly composed of artificial buildings and cement pavement which are clearly distinguished from natural surfaces favor heat accumulation (Sarrat et al., 2006;Coseo et al., 2015). Recent research shows that in urban regions, Urban Heat Island (UHI) can be observed by a difference of about 1.5-2°C compared to rural area temperatures, and about 2.0-2.5°C difference can be observed over densely built-up areas (Falasca et al., 2018). The UHI has a negative effect on the planetary boundary layer (PBL) height (Lin et al., 2008a) and surface wind speed, which affects the transport and dispersion of pollutants. Overall, the UHI changes the local atmospheric dynamic (Falasca et al., 2016), significantly influences local air pollution, reduces visibility, increases water usage, and enhances heat-related morbidity (Gedzelman et al., 2003;Lai et al., 2009). In modeling studies, detailed information on urban parameters is critical for the simulation of the UHI effect. Studies conducted in Taiwan have found that the urban canopy model can improve the prediction of UHI intensity, boundary layer development, land-sea breeze (Lin et al., 2008b), and precipitation (Lin et al., 2008a). Air pollution prediction is crucial in order to inform a large fraction of the population and take adequate short-and long-term measures to improve air quality. The use of air quality modelling tools at high resolution is now common but remains a challenge, because buildings create local modifications in air flows and modify surface energy budgets and roughness, leading to very complex processes which make air quality simulation particularly difficult in the near-ground layers (Best et al., 2005;Liu et al., 2006;Bessagnet et al., 2014;Barlage et al., 2016). Chemical transport models usually require numerical weather prediction (NWP) models, which provide meteorological input data (Molteni et al., 1996;Tuccella et al., 2012).

High-resolution urban canopy parameters databases are becoming more and more available for cities (Salamanca et al., 2009a;Salamanca et al., 2009b). NWP models with different physics parameterizations have been widely used in recent decades to simulate air quality over urban regions, especially the widely used community mesoscale Advanced Research Weather Research and Forecasting (ARW-WRF) model. Studies show that physics parameterizations play a critical role both in meteorological and air quality simulation or forecast (Miao et al., 2010;Fan et al., 2008;Liu et al., 2014;Valari et al., 2014;Markakis et al.,2016). For example, the Planetary Boundary Layer (PBL) height is determined by the heat and momentum exchanges between the PBL and the surface. In meteorological models, the PBL height is calculated by the PBL scheme. Therefore, the PBL scheme is a crucial factor in simulating the formation and evolution of air pollution (He et al., 2015;Wang et al., 2014;Wang et al., 2019). Studies on different PBL parameterization schemes have shown that an accurate description of the meteorological conditions within the PBL via an appropriate parameterization scheme is important for air pollution modeling (Hu et al., 2010). Some studies have presented the influences of meteorological models on pollutant concentrations; they estimate that uncertainties of PBL heights from the different models are one of the major sources of the differences in Particulate Matter (PM) concentrations (Hariprasad et al., 2014;Tyagi et al., 2018). Kleczek et al. compared three non-TKE (Turbulent Kinetic Energy) schemes and four TKE closures schemes; they found that non-TKE schemes tend to produce higher temperatures and wind speeds than in TKE schemes, especially during nighttime.

Barlage et al. also had a similar result between TKE and non-TKE schemes at night and found that PBL schemes have little effect on model performances in urban locations during daytime. Some other studies indicate that most PBL schemes slightly overestimate near-ground wind speed (Borrego et al., 2013); a possible reason could be the omission of the effect of unresolved topographic features on the momentum flux (Anderson et al., 2010).

For the first time, the chemistry transport model CHIMERE model has been used with a very low first layer of about 4m to simulate the air quality. In order to understand the impact of physics parameterizations and improve high-resolution air quality simulation, various WRF configurations have been tested over the Paris region during a critical pollution episode occurring from 27 th November to 4th December 2016. In this study, we examine three different PBL schemes: (1) the Mellor-Yamada-Janjic scheme (MYJ), ( 2) the YonSei University scheme (YSU), and (3) the Bougeault and Lacarrere scheme (Boulac); and three canopy schemes, (1) a reference scheme (Bulk) which does not consider urban canopy parameters, (2) a single urban canopy model (UCM) which simply defines the urban geometry as two-dimensional street canyons but considers the three-dimensional nature of urban morphologies, and (3) a multilayer urban canopy model considering building effect parameterization (BEP). BEP was developed by Martilli et al (2002). and allows the direct interaction between buildings and low atmospheric layers and therefore is more realistic in reproducing urban effects (Salamanca et al., 2009a). This work will inform the air quality community on our ability to assess and understand the atmospheric transport of air pollutants over cities for operational forecast with air quality models.

Model Description and Experiment Design

WRF Model Description

Several studies (Flaounas et al., 2010;Patricola et al., 2012;Vigaud et al., 2009) demonstrate the importance of physics parameterizations in driving air quality modelling outputs. As the first step of this work, the WRF model (Version 3.9.1) is used to simulate the meteorological conditions. The WRF model provides several options for the parameterizations (details can be found in the WRF user manual) of physical processes, including multiple longwave and shortwave radiation schemes, surface layer schemes, land surface schemes, urban canopy schemes, and boundary layer schemes. The initial and boundary conditions are issued from the Global Forecasting System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP), available at a 0.25° × 0.25° resolution at six-hourly time steps. The spectral nudging in WRF simulation is a way to constrain large-scale circulation and constrain the model to be more consistent with observations (Bowden et al., 2012).

However, strong nudging may filter out extreme episodes, since nudging pushes the model toward a relatively smooth state (Glisan et al., 2013). Therefore, a weak spectral nudging above PBL has been tested to improve the accuracy of the downscaled fields but not filter out the simulation of extreme episodes.

The experiment set includes 11 simulations, which are conducted for a winter period from 27 th November to 4 th December 2016 and are designed to test the sensitivity of the surface meteorological variables and pollutant concentrations at these different physics parameterizations.

Table3 WRF provides several urban canopy schemes options, the UCM and BEP has been chosen in this study and its structure diagram is shown in Figure3.1. The UCM is a single urban canopy layer model used to consider the effects of urban geometry on the surface energy balance and wind shear for urban regions (Kusaka et al., 2001;Kusaka et al., 2004). This model includes shadows from buildings; canyon orientation; the diurnal variation in the azimuth angle; the reflection of short and long wave radiation; wind profiles in the canopy layer; anthropogenic heating associated with energy consumption by human activities; and multi-layer heat transfer equations for roof, wall, and road surfaces. The BEP is the multilayer canopy layer scheme.

This scheme considers the sub-grid wall, roof, and road effects on radiation and fluxes but does not include the energy exchange between the inside and outside of buildings.

The MYJ PBL scheme uses the 1.5-order turbulence closure model of Mellor and Yamada (Mellor et al., 1982) to represent turbulence above the surface layer (Janjic et al., 1990;Janjic et al., 2002). The MYJ scheme determines eddy viscosity K-coefficients from prognostically calculated turbulent kinetic energy. The YSU PBL scheme is a first-order non-local scheme that uses a parabolic K-profile in an unstable mixed layer with the addition of an explicit term to treat the entrainment layer at the top of the PBL (Tyagi et al., 2018). Hong et al. enhanced mixing in the stable boundary layer by increasing the critical Bulk Richardson number from zero to 0.25. The Boulac PBL scheme was developed by Bougeault and Lacarrè re (Bougeault et al., 1989) and is also a TKE prediction scheme and designed to work with a multi-layer urban model.

The Noah-mp land surface model (Niu et al., 2011) is the successor of the Noah model (Chen et al., 1996;Chen and Dudhia, 2001). There are multiple options for land-atmosphere interaction processes in Noah-mp, which users can select (Barlage et al., 2015). These options include a variety of formulations of processes such as stomatal conductance, runoff generation, snow and soil parameterizations, and radiative transfer in the canopy.

Description of the CHIMERE Model

The chemistry transport model (CTM) CHIMERE (Version 2013) (Menut et al., 2013) directly taken from the WRF output and capped at a minimal value of 20m to avoid unrealistic simulations. In CHIMERE, the K z is calculated using the parameterization of Troen and Mahrt (1986) in Equation (3.1):

( ) #( )
where k is the von Karman constant set to 0.41, z is the altitude, h is the boundary layer height, and w s is the vertical scale given by the similarity formulae. CHIMERE provides the hourly concentrations of tens of species at a regional scale; detailed descriptions of the model can be found in several previous studies (Bessagnet et al., 2004;Menut et al., 2012;Bessagnet et al., 2017;Mailler et al., 2017). The aerosol module accounts for both inorganic and organic species, such as sulfate, nitrate, ammonium, primary organic matter, element carbon, secondary organic aerosol, sea salt, dust, and water. The aerosol size is assuming discrete aerosol size sections and that the particles are internally mixed. Nine diameter bins, from 10nm to 10μm, with 5 bins below 1 μm, are used (Couvidat et al., 2018).

Domains Setup and Observations Data

In order to calculate realistic meteorological input variables and pollutant concentrations, the system is configured with four nested-grid domains (Figure3.2) over Europe, France, the north of France, and Ile-de-France, with cell sizes of 45 (d01), 15 (d02), 5 (d03), and 1.67km (d04), respectively. The finest grid domain (d04) used has 83 × 77 grid points and covers the whole Ile-de-France region, including urban, sub-urban, and rural areas. The WRF simulation is discretized vertically on 52 vertical levels from the surface to the top (50hPa), with 20 levels below 1000m and the lowest level at 2m. In order to take full advantage of the physical parameterizations, the CHIMERE simulation is performed with the same horizontal domains as the WRF; the 52 vertical levels from the WRF simulation are projected onto 20 levels in CHIMERE. The ground layer of the CHIMERE domains is at 999.5hPa from about 4m up to over 5000m, with 15 levels below 1000m. Such a low ground level could effectively reproduce the transport and diffusion of pollutants in a real urban environment. Figure3.3 Maps of the meteorological and air quality monitoring stations.

Results and Discussions

Urban Parameters and Nudging Tests

The urban canopy model in the WRF simulation affects the modeling of several important meteorological variables for air quality issues (e.g., 10m wind speed W10, 2m surface temperature T2, and PBL height). It changes the vertical distribution of atmospheric pollutants by modifying the boundary layer height and mixing. Geometric and thermal parameters, including anthropogenic heat, thermal conductivities, and heat capacity, have a significant effect on the transfer of momentum between the atmosphere and urban surface (Kim et al., 2015). However, the representativeness of these parameters aggregated over a CTM grid cell remains highly uncertain, especially with a limited number of urban land-use types. The urban parameters (Table S1) used here are based on a previous study in Paris region (Allen et al., 2010;Kim et al., 2013) and default urban parameterization (URBAN-PAM). The TBL tables in the WRF code are defined as URBAN-PAM1 and URBAN-PAM2, respectively. The RRTMG shortwave and longwave radiation scheme, MO similarity surface layer scheme, Noah land surface model, BEP urban canopy model, and MYJ boundary layer scheme are used for all simulations in this section. Figure3.4 presents the time series of hourly-averaged T2 and W10 at Sirta station. The results show that simply modifying the values of the parameters has a strong impact on the outputs. The T2 presents a more positive correlation (0.86 than 0.81) in URBAN-PAM1 than in URBAN-PAM2, while the W10 has a more negative bias (-1.51m/s than -1.42m/s) in URBAN-PAM1 than in URBAN-PAM2. A weak spectral nudging above the boundary layer for all the domains is also tested in this part.

Figure3.5 indicates that the spectral nudging technique is effective in improving the model skills. The performance benefits in Paris from the assimilation of meteorological data at Roissy airport in the global meteorological fields. Thus, URBAN-PAM1 and spectral nudging are used for all our sensitivity tests, which are discussed in the following sections.

Figure3.4 Time series of 2m temperature and 10m wind speed at Sirta station.

Figure3.5 Time series of 2m temperature and 10m wind speed at Montsouris station.

Impact of the Urban Canopy Model

Ground Meteorological Variables

The RRTMG shortwave and longwave radiation schemes, the MO similarity surface layer scheme, and the Noah land surface model are used for all the simulations in this section. The whole period (27 th November to 4 th December 2016) is divided into two periods: a pollution episode (PE) from 30 th November to 2 nd December; and other days than the PE, which are defined as regular days (RD). No rainfall occurred over both then PE and the RD. Figure3.6 presents the maps of averaged T2 simulated based on the Bulk, UCM, and BEP schemes in this period. All the schemes show a higher T2 in the urban region than the rural, and the BEP scheme displays the strongest UHI effect in the urban region, which gives a positive bias of up to 1°C compared to the other two schemes. The Bulk and BEP schemes perform similarly in rural areas and display a negative bias (1-3°C by Bulk and 1-4°C by BEP) compared with urban areas, but the UCM scheme gives the highest T2 in rural areas, displaying a negative bias of about 0.5-1°C compared with urban regions. A possible reason could be the overestimation of surface heat flux (SHF) in the rural areas. Figure3.7 presents the averaged SHF in this period.

The Bulk and BEP schemes display obvious differences between urban and rural regions, with a negative bias (around -25W/m 2 to -35W/m 2 ) in rural compared to urban areas. The UCM scheme just produces a negative bias of -10W/m 2 and -15W/m 2 , respectively, in rural and urban areas, which could give an explanation of the overestimation of T2 in rural areas.

Figure3.6 Maps of the mean 2m temperature (°C ) over the period from 27 th November to 4 th December.

Figure3.7 Maps of the mean surface heat flux (W/m 2 ) over the period from 27 th November to 4 th December.

Figure3.8 Time series of 2m temperature (°C) between observations and different urban schemes at Montsouris, Roissy, and Sirta stations.

Figure3.9 shows the maps of the averaged W10 simulated for the Bulk, UCM, and BEP schemes in this period. All the schemes give a higher surface wind speed in rural areas than in urban regions; the Bulk scheme shows the weakest gradient from urban to rural areas, with a negative bias for 0.51m/s; UCM gives a relative higher negative bias than Bulk from 1m/s to 2m/s; and the BEP scheme had an obviously strongest impact from cities, displaying a negative bias around 3.5m/s. The comparison between the observations and models for W10 are displayed in Figure3.10. The observed W10 varied between 0.6m/s to 7.6m/s, 0.1m/s to 9.4m/s, and 0.2m/s to 5.5m/s, with an average of 2.3m/s, 1.5m/s, and 2.8m/s in three meteorological observation stations (Montsouris, Roissy, Sirta), respectively. Both the observed and modelled W10 display much lower values during the PE than the RD. In contrast with the T2, the W10 does not display remarkable differences between stations. The BEP scheme tends to produce the lowest W10 throughout the whole period for all the stations.

Bulk and UCM performed well for both the PE and RD. A possible reason could be due to the roughness length, which is not directly dependent on urban morphology for the UCM scheme, while the momentum sinks and drag force estimated by the BEP scheme depend on urban morphology in this study. Low wind speed leads to weak natural circulation and the accumulation of heat in the lower urban boundary layer, which in turn enhances the warming of the urban area (Wu et al., 2017). This would explain why BEP has a relative higher T2 Figure3.9 Maps of the mean 10m wind speed (m/s) over the period from 27 th November to 4 th December.

Figure3.10 Comparison of the time series of 10 m wind speed (m/s) between the observations and different urban schemes at Montsouris, Roissy and Sirta stations.

Air Quality Modeling

In this study, NO 2 , PM2.5, and PM10 concentrations are used to evaluate the impact of physics parameterizations on air quality simulations. NO 2 , which comes mainly from automobile exhaust emissions in cities, is selected to be an indicator of local pollution.

Comparing the evolution and spatial patterns of NO 2 versus the PM2.5 and PM10 gives an indication of the local contribution during this episode. Figure3.11 presents the differences in pollutants distribution between each canopy scheme. The results show that, compared to the Bulk scheme, the pollutant concentrations from the BEP scheme were lower in the city center, but displayed an opposite behavior compared to the UCM scheme. The UCM scheme displayed higher pollutant concentrations in the urban area and lower pollutant concentrations in rural areas. Figure3.12 presents the time series of NO 2 mass concentrations for all the experiments compared to the observations for the four stations (the time series of PM2.5 and PM10 mass concentrations can be found in Annex A Figure S1). The average biases of the four stations are given in Table3.2, and the time series of the biases are displayed in Figure S2.

The results show that the BEP scheme gives the best performance for almost the whole period, especially during the PE. All the schemes can effectively predict air pollution during the RD, but they usually overestimate pollutant concentrations for all the stations during the PE; all the schemes had their worst performance in NO 2 prediction, which means that the canopy schemes do not simulate well the emission of local air pollution. Figure3.13 gives the vertical diffusion coefficient at daytime and nighttime in the RD and PE. The Bulk scheme has the highest vertical diffusion rate among the three schemes during the RD. Figure3.14 presents the vertical profile of the NO 2 and PM2.5 mass concentrations under 200m; both NO 2 and PM2.5 show the lowest vertical mass concentrations at low layers by the Bulk scheme and the highest vertical mass concentrations by the BEP scheme during the RD, which agrees with the vertical profile of Kz in CHIMERE. At 06:00 UTC on 30 th November, the Kz displays very low values at ground layers for all three schemes but rises rapidly in the BEP scheme from the third layer. Figure 3.14c also presents high pollutant concentrations at the ground layer for all three schemes but decreases rapidly in the BEP scheme from the third layer. Looking at the NO 2 concentrations, there is clearly an added value of using a low first model layer in the model to enhance the concentrations close to emission sources related to ground sources such as road traffic. In the models, it is more common to use a first layer of about 20m to 50m (Toll et al., 2000;Baklanov et al., 2008). Figure S3 presents the hourly time series of NO 2 as an average of all the air quality monitoring stations between the observations and simulations, with first layers of about 4m, 18m, and 40m (based on the best physics parameterizations in this study). An extremely low vertical diffusion rate leads to high concentrations of pollutants, which is one reason for the strong overestimations at the surface layers at this time. In general, the BEP scheme has the best performance among the three schemes, but all the schemes strongly overestimate the concentrations at the beginning of the PE, especially the UCM scheme. A low near-ground wind speed usually indicates low diffusion rates, which means the BEP scheme involves higher pollutant concentrations than for the other two schemes, but our study shows the pollutants concentrations is not found to be positively correlated with the W10. A possible explanation could be the underestimation of PBL height, which leads to a low diffusion rate at this period; a detail discussion about the impact of the PBL height is in the next section.

Figure3.11 The differences in the daily averaged NO 2 (μg/m 3 ), PM2.5 (μg/m 3 ), and PM10 (μg/m 3 ) between different schemes from 27 th November to 4 th December 2016.

Figure3.12 Time series of NO 2 mass concentrations (µg/m 3 ). No1,[START_REF] Yao | The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> Chapter V 159/191 directly emitted from vehicles[END_REF]3,4

Ground Meteorological Variables

A comparison of the performances of various PBL schemes in simulating the surface meteorological variables (T2 and W10) is presented in this section. Figure3.16 and Figure3.17

shows the time series of T2 and W10 based on several PBL schemes. The YSU-Bulk and Boulac-BEP have a slight positive bias compared to MYJ, but the YSU-UCM scheme had a cooling effect compared to the MYJ-UCM scheme during the PE. In the MYJ-BEP and Boulac-BEP schemes, it can be observed that the models always underestimate the wind speed compared to the observations in this period. The Boulac-BEP scheme produces a lower W10 than MYJ-BEP throughout the whole period, especially during RD. In contrast, W10

does not show significant differences between the MYJ and YSU schemes coupled with the other two Bulk and UCM canopy models. Table3.3 summarizes the performances of the models, including the mean bias (MB) and mean linear correlation coefficient (R) of meteorological variables. These schemes display a high correlation which approximately reproduces the measured trend. The three experiments with the MYJ boundary layer scheme display a negative bias in W10 in Roissy and a negative bias in T2 in both Roissy and Sirta stations. A less negative bias is noticed with the simulations using the Boulac-BEP scheme compared with the others for the whole period, but it still produces a positive bias during nighttime in the PE. The reason could be that the Boulac PBL scheme takes the turbulent exchange affected by complex terrain into account in the calculation of the turbulent diffusion coefficient, leading to a smaller bias in T2 prediction (Li et al., 2006). The Boulac-BEP scheme slightly improves the results with respect to the MYJ-BEP scheme for the W10, but still strongly underestimates by 1.53m/s at Roissy station. The correlation indicates the PBL schemes coupled with the urban canopy schemes (MYJ-UCM, YSU-UCM, MYJ-BEP, and Boulac-BEP) are more accurate than those with no urban canopy scheme (MYJ-Bulk and YSU-Bulk). In contrast to the PBL height results, the model performances for T2 and W10 based on various PBL schemes are mixed. Therefore, it is not straightforward to agree on a definitive best choice for the PBL scheme.

Figure3.16 Time series between the observed and modelled T2: (a) and (b) represent Roissy

and Sirta station, respectively; 1, 2, and 3 represent each PBL scheme coupled with each urban canopy scheme. 1.23µg/m 3 , and -8.63µg/m 3 , respectively. This indicates that both the urban canopy and PBL schemes play an important role in the PE forecast for urban regions.

Table3.4 Statistics of surface variables: mean bias (MB), root mean square error (RMSE), and mean linear correlation coefficient (R) between the simulations and observations of NO

Impact of Land surface model

In order to understand the impact of land surface model (LSM), a widely used Noah LSM scheme (version 3.0) and an advanced LSM model (Noah-mp) are analyzed in this section.

Compared with Noah, Noah-mp separating the vegetation canopy from the ground surface which could solve the critical problems such as overestimation of runoff, too dry deep soil, too impermeable frozen soil, and too rapid ablation of snow, etc. Other physical parameterizations were based on the formal study which means BEP urban canopy model and Boulac PBL model (Bou-BEP) are selected in this section.

We evaluated the modelled T2 and W10 against observations in three stations. As illustrated by Figure3.20, Noah-mp scheme gives a warmer surface skin temperature compared to the Noah scheme and its improved T2 simulation in the nighttime during the PE. Some researches (Niu et al., 2011;Salamanca et al, 2018) reported that Noah-mp produces a weaker surface exchange coefficient that Noah scheme leads a higher heating of the land surface during the midday hours. diffusion conditions, the use of the Noah-mp scheme makes negligible differences in W10 simulation.

Figure3.20 Time series of 2m temperature (°C) between observations and different urban schemes at Montsouris, Roissy, and Sirta stations.

Figure3.21 Comparison of the time series of 10m wind speed (m/s) between the observations and different urban schemes at Montsouris, Roissy and Sirta stations.

The impacts of the LSM on NO 2 and PM2.5 simulation is illustrating in Figure3.22 (The PM10 can be found in Figure S10). The results show a very slight difference during regular days in all four station for both NO2 and PM2.5 simulation but gives some differences at the PE in FR04034 and FR04143 station. The possible reason could be the Noah-mp model produces stronger amplitude of ground heat flux by removing the exponential decay of the surface soil thermal conductivity with vegetation cover fraction (Niu et al., 2011). Overall, Noah-mp is demonstrated to be a viable research tool through which the role of a specific

Conclusions

This study evaluates the impact of three urban canopy parametrization schemes (Bulk, UCM, For the first time, chemistry transport models have been used with a first layer at 4m to better account for physical processes within the suburban layer in relation to ground emissions such as road traffic. Clearly, the sharp gradient of concentrations shows the added value of using such a low level in urban areas. This study shows the high sensitivity of air quality simulation to meteorological model outputs during stable conditions favorable to the occurrence of PM episodes.

Three urban canopy models could reproduce the T2 effectively in the daytime; the Bulk and UCM model reproduced the W10 well, but the BEP model underestimates W10 for the whole episode, indicating an important impact of buildings on the diffusion rate for this scheme. The UCM scheme overestimates the surface heat index in rural areas, leading to a slight negative bias for T2 in rural versus urban areas. T2 displays ambiguous results, while all schemes reproduced results well at Sirta station but overestimated during nighttime at Montsouris and Roissy during a pollution episode. This could be due to the use of a single urban category in this study, which leads to a discrepancy that causes more spatial variability in urban parameters. NO 2 , PM2.5, and PM10 were selected to evaluate the effect of physics parameterizations on air quality simulation. Large differences were found in the simulation of pollutant concentrations, with an overall overestimation during the pollution episode.

The six PBL sensitivity tests display an underestimation of the PBL height when compared with the observations issued from the Lidar. All the sensitivity tests, except Boulac-BEP, could not effectively simulate the PBL height during the pollution episode. The two PBL schemes coupled with the BEP scheme still underestimated the W10 for the whole period, but the Boulac-BEP scheme improved the simulations compared with the MYJ-BEP scheme.

The Boulac-BEP scheme had a significantly better performance than the other schemes for the simulation of pollutant concentrations, indicating that both the canopy parametrization and PBL schemes have a positive effect on air quality simulation in urban regions, especially during pollution episodes. The Noah-mp land surface scheme has slightly impact for both meteorological and air quality simulation. An advanced urban category could improve air quality prediction in urban regions. vertical layer in WRF and 9 vertical levels in CHIMERE, the first layer height is approximately at 40m. A fifteen days simulation including a three-day heavy pollution episode (PE) illustrates the impact of vertical grid resolution. The first layer height has a relatively small impact on meteorological simulation and the main differences of meteorological variables were observed at nighttime. However, a consistent improvement in chemistry transport modelling were displayed with refined vertical resolutions and the first layer height, compared to L12 and L40, the average improvement from L4 case in terms of RMSE are 23.26% and 25.09% on regular days (RD) defined as -without pollution episodes‖ and 62% and 129% during PE respectively.

In general, the ability of the model with low vertical grid resolution to correctly predict air quality is reasonable on RD or light pollution events, while it fails to predict correctly the pollutant concentrations for heavy polluted situations. The performances were improved with refined vertical resolutions particularly with extra fine first layer height at 4m.

Ré sumé

Les modè les à mé so-é chelle CHIMERE et Weather Research and Forecasting (WRF) ont é té utilisé s pour é valuer l'impact de la ré solution de la grille verticale et de la hauteur de la premiè re couche dans la modé lisation chimie transport. Trois configurations de modè les (L4,

Keywords: air quality; vertical resolution; first layer height; diffusion; urban

Introduction

The prevention and control of environmental pollution has become an important task in the development and construction of the country, and the prevention and control of air pollution has become the top priority (Lu et al., 2020). Over the past decades, European countries deepen efforts to decouple air pollution from economic growth and the goal is to build an ecological civilization society (Ivanova, 2011;Koolen and Rothenberg, 2019b;Nam et al., 2010). Studies show many air pollutants have decreased substantially since mid-1980mid- 's (Grennfelt et al., 2020;;Lelieveld et al., 2019;Renzi et al., 2017), for example, observation data show that in Europe, the concentration of PM2.5 drops by 2.5μg/m 3 per year over the past decades (Gozzi et al., 2017). However, some reports indicate more than 400,000 people die prematurely due to air pollution each year in Europe and this number is one million in 1990 (Guerreiro et al., 2015). Air pollution is still one of the biggest threats to the health of Europeans. In many European cities, the concentration of main pollutants still exceeds the reference value of European Union (Barsova et al., 2019;Xu et al., 2017) and over the hemispherical background concentrations (Vautard et al., 2009). Therefore, regional joint prevention and control play an important role in improving air quality. At region and urban scale, mesoscale chemistry transport model (CTM) are widely used to assess the efficiency of such policies in controlling air pollution (Ma et al., 2020). With the continuous improvement of pollution sources and meteorological knowledge, many studies have proved the important role of horizontal resolution in improving model capabilities (Jensen et al., 2017;Martins, 2012;Russell et al., 2019;Thunis et al., 2016b). However, there is still a lack of research on the effect of vertical grid resolution on the chemical transport processes. Accurate simulations of near-surface parameters particularly the representation of turbulence is difficult but very important on vertical mixing of meteorological and chemistry transport modelling. A spatial resolution increase enhances the variability of the concentrations, but also the amplitude of the errors due to meteorology, vertical resolution, land use or other factors (Aligo et al., 2009;Vautard et al., 2007). Since human activities are mainly concentrated within 50 meters from the ground, it is particularly important to improve the model's representation of pollutants in low boundary height. Research found that adding an extra vertical layer close to the surface is important for high nocturnal concentrations under stable boundary layers (Menut et al., 2013b).

In this chapter, an evaluation of three vertical grid resolution configurations and a first layer height, used to simulate a winter period including a short heavy pollution event in Paris region is proposed.

Method

The mesoscale Weather Research and Forecasting (WRF) model in Version3.9.1 and CTM model CHIMERE in Version2013 are used to simulate the meteorological parameters and pollutant concentrations over Paris. The horizontal domain setting is same as that of in Chapter II, with four nested-grid domains over Europe, France, the north of France, and Ile-de-France, with horizontal resolutions of 45km, 15km, 5km, and 1.67km, respectively.

The horizontal resolution of CHIMERE is consistent with WRF. Details can be found in Chapter III. A winter pollution event occurred between 30 th November to 1 st December 2016.

In Chapter III, we analyzed the impact of physical parameterizations on high resolution meteorological and chemistry transport modelling and summarized the best parameter settings.

Which means the rapid radiative transfer model for GCMs (RRTMG) shortwave and longwave radiation scheme; Monin-Obukhov (MO) similarity surface layer scheme; multi-layer urban canopy (BEP) scheme and Bougeault and Lacarrere (Boulac) boundary layer scheme are used as the physical parameter settings in this study. In order to better evaluate the impact of the vertical grid resolution and the first layer height on the air quality prediction on Regular days (RD) and Pollution episode (PE), the time series has been extended from eight days to fifteen days (from 20 th November to 4 th December) compared to the Chapter III, including 12 days RD from 20 th November to 29 th November and 3 rd December to 4 th December, and a three days heavy winter PE from 30 th November to 2 nd December .

The vertical resolution of WRF is projected over CHIMERE. WRF having more vertical layers than CHIMERE. Therefore, three different vertical resolutions are defined for monitoring stations issued from AIRPARIF were used. Those stations are urban or sub-urban background stations, the locations of stations can be found in Figure4.1.

Figure4.1 Locations of the meteorological and air quality monitoring stations.

Here, taking the relative change in % of the RMSE as the evaluation criteria of the model performances which defined as (4.1):

(%) (4.1)
Where refers to the RMSE of the test case and refers to the RMSE of the reference case.

Results and Discussion

Surface meteorological and pollutants concentration

Meteorology

The aim of this study is to assess the ability of the WRF-CHIMERE suite in simulating meteorological parameters and the main pollutant concentrations with different vertical grid resolution. In Figure4.2, the maps of 2m temperature (T2), 10m wind speed (W10) and surface relative humidity (RH) are displayed for the finest domain over the whole period.

Although the absolute values are different, the regional patterns are similar for the three cases.

For the simulation of T2, all three cases show obvious urban heat island effects of the domain, the absolute values in urban regions are approximately 2°C higher than the surroundings, and proportional to the height of the first layer (T2 L40 > T2 L12 > T2 L4 ). The W10 values increase with an increasing first layer height in rural areas. However, in urban areas, L12 displays the highest W10, and L4 trends to produce the lowest W10 in cities. The momentum sinks depend on urban morphology in BEP urban canopy scheme, this indicates that an ultra-fine

Chapter IV 102/191 first layer height could overestimate the drag effect of the realistic urban morphologies. The regional pattern of surface RH is inversely proportional to T2, with the highest RH simulation both in urban and rural region for L4 and lowest for L40. Table4.1 lists the range and average values of meteorological parameters for the three cases. The relative bias (RB) of L12 and L40 compared to the reference case (L12-L4 and L40-L4) are 2.17% and 22.95% for T2, 4.01% and -12.69% for W10, -0.12% and -2.72% for RH, respectively. The RB of L40-L4 clearly

shows that vertical grid resolution has a strong impact of meteorology particularly for T2

simulation, but there is no obvious impact on meteorological parameters simulations by refining fine first layer according to the results of L12-L4. the performances of meteorological parameters simulation, all three cases display gradient concentrations between the urban and rural areas. In the case of NO 2 , the surface concentrations in urban area and near the traffic lines are much higher. Table4.2 shows that unlike the vertical grid resolution has a relatively small impact on meteorology, RB in L12-L4 and L40-L4 are both substantial in air quality simulations. The RB are -10.68% and -39.34%, respectively. In the case of PM2.5 and PM10, surface concentrations from the individual cases again displays large differences. The RB of L12-L4 and L40-L4 are -4.61% and -36.98% for PM2.5, -4.01% and -35.85% for PM10, respectively. The dispersion of the particulate matters away from the cities are uneven. Regional patterns show the lowest surface particulate matters in near urban surroundings and southwest regions, this because those areas are mainly covered by broad-leaf forests. The range of pollutants provide by the individual cases are also worth noting. Taking NO 2 as an example, the maximum concentrations values from L4, L12 and L40 are 136.34μg/m 3 , 82.56μg/m 3 and 62.87μg/m 3 respectively. The value of the L4 case is 1.65 time than L12 and more than twice value of the L40. This indicates the potential improvement of the model's ability on pollution events prediction through an improvement of the vertical grid resolution.

Vertical profiles

The averaged vertical profiles of vertical diffusion coefficient Kz, NO 2 , PM2. Figure4.4 Vertical profiles of Kz(m 2 /s), NO 2 , PM2.5 and PM10 (μg/m 3 ) from 20 th Novermber to 4 th December over Paris region.

Comparison of model results with observations

Meteorological simulations

The comparison of model simulated and observed T2 and W10, as well as planetary boundary layer height (PBLH) are presented in Figure4.5. During this period, a PE from 30 th November to 2 nd December was reported according to the PREV'AIR system. For L4 and L12 cases, the model reproduces well T2 on RD and overestimated T2 on PE by up to 3.08°C for L4 and 4.73°C for L12. The statistical evaluation (Table4.3) shows that differences of model performance for T2 between L4 and L12 are small, both with high correlation (0.97 and 0.96) and low root mean square error (RMSE) shows that those two cases results reproduced realistic T2 fields. However, there is an overall overestimation of T2 for L40 case up to 9.08°C at night of 1 st December. Table4.3 shows that the mean bias (MB) for L40 case are 4 and 3.49 times compared with L4 and L12. The second example of predictions refers to the simulation of W10. The L12 case simulated the highest W10 most of the time, three meteorological observation stations are all urban background stations. These results match with the maps of W10 in the urban region. All three cases underestimate W10 during the whole period, the negative biases are 1.95m/s, 1.23m/s and 1.96m/s respectively. The L12 shows better performance on W10 prediction than other two cases. The observed Planetary Boundary Layer Height (PBLH) is very often below 200m, leading to a weak mixing of pollutants and therefore an accumulation of pollutants closes to the surface. For the PBLH, the model performance is poor, the highest correlation coefficient was found in L40, however, this case overestimated PBLH approximately 160m on PE, this will cause an overestimated mixing rate and thus underestimate the pollutant concentration. In order to better understand the main differences, diurnal cycles are presented in Figure4. Hours of day Figure4.7 Time series between the observed and modelled NO 2 , PM 2.5 , PM 10 (μg/m 3 ). Figure4.8 Time series of bias between L12 to L4 (L12-L4) and L40 to L4 (L40-L4).

Table4.4 Statistics of surface variables: mean bias (MB), mean linear correlation coefficient (R) and root mean square error (RMSE) between the simulations and observations of NO

In order to better evaluate the model performance during the PE, statistics of individual cases can be found in Table4.5. On RD, the RB of L12 to L4 is small, the values are -4.79%, 2.43% and 3.84% for NO 2 , PM2.5 and PM10 respectively. However, such values are large in L40-L4, which are -28.25%, -34.51% and -33.74%. Table4.6 reports the operational time consumed by the three cases for simulating a single day meteorological and chemical transport processes for all domains. The operating processes of WRF and CHIMERE both consist of two steps: a pre-processing step and a main operating step.

The pre-processing step takes a very short time, ranging from tens of seconds to several minutes.

Here, a single-day operational time often refers to the total time taken to complete these two steps. The difference between the L4 and L12 cases for meteorological modelling is small, only 3 minutes but L40 can save 14 minutes compare with L4. For chemistry transport modelling, the difference between the L4 and L2 cases is similar with their meteorological process, while the difference for operational uses of the L40 case is more straightforward. Compared with the L4 case, 44 minutes are saved, the CPU time is divided at least by a factor 2.

Results show acceptable model ability at low vertical grid resolution on RD. Therefore, for the high-resolution small domain cases, it is obvious that high vertical grid resolution is a better setting. In particular, the reasonable first layer height may greatly improve the simulation results. For a long-term and large-scale simulation, considering the cost of computing resources, a low vertical resolution configuration should be a more suitable solution for practical use.

Table4

Conclusions

This study presents a sensitivity analysis of the impact of vertical grid resolution and the first layer height in meteorological and chemistry transport modelling during a pollution episode at urban scale. The simulations are carried out over a fifteen days winter period from 20 th November to 4 th December 2016 including a pollution episode (PE) from 30 th November to 1 st December and the rest of days is defined as regular days (RD).

The maps of meteorological parameters and main pollutants show similar regional patterns for the three cases. The differences between the different model configurations are relatively small on meteorological parameters. The relative bias (RB) of L12 and L40 compare with L4 (L12-L4 and L40-L4) are 2.17% and 22.95% for 2m temperature (T2), 4.01% and -12.69% for 10m wind speed (W10), -0.12% and -2.72% for surface relative humidity (RH), respectively. The differences between three configurations in chemistry transport modelling are more obvious. The RB of L12-L4 and L40-L4 are -10.68% and -39.34% for NO 2 , -4.61% and -36.98% for PM2.5 and -4.01% and -35.85% for PM10, respectively.

However, for the planetary boundary layer height (PBLH), L40 case reproduces higher PBLH than others in most of the time of this period and overestimated PBLH about 160m on PE, leading to a stronger vertical mixing than for L12 and L4 simulations. The differences between the three configurations in meteorological parameters simulations are larger during nighttime than that for daytime.

Introduction

Air pollution is one of the biggest public health hazards worldwide responsible for over 6 million deaths every year ( Forouzanfar et al., 2016;[START_REF] Lim | Personal exposures to PM2.5 and their relationships with microenvironmental concentrations[END_REF][START_REF] Valari | EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub&gt[END_REF]. In cities, air quality is a major concern for citizens and city managers (Baklanov et al., 2007).

With the development of computer performance and a better knowledge of the meteorology and pollution sources, meteorological and air quality modelling has been widely used in the last Chapter V 122/191 decades, particularly in urbanized and industrialized areas [START_REF] Mailler | Impact of the vertical emission profiles on background gas-phase pollution simulated from the EMEP emissions over Europe[END_REF]. Today, High resolution mesoscale chemistry-transport models (CTM) such as CMAQ, CAMx or CHIMERE have achieved sufficient accuracy to be considered for urban air quality predictions (Byun and Schere, 2006b;Fillingham, 2019;Sokhi et al., 2018;Terrenoire et al., 2015).

However, patterns of urban air pollution are rather variable and spatially heterogeneous [START_REF] Wolf | A very high-resolution assessment and modelling of urban air quality[END_REF], the interactions between urban and the atmosphere remains challenges (Letzel et al., 2008), especially on modelling airflow and dispersion within the urban canopy [START_REF] Baker | A study of the dispersion and transport of reactive pollutants in and above street canyons-a large eddy simulation[END_REF]. Turbulence plays an important role on the vertical mixing of pollutants and other physical parameters (Laurent Menut et al., 2013a;[START_REF] Pierce | Dynamic evaluation of a regional air quality model: Assessing the emissions-induced weekly ozone cycle[END_REF], but the representation of turbulence is difficult to predict particularly close to the ground levels. All CTMs must account the problem of turbulence, even if turbulent processes are still not fully understood: for example the statistical approaches of turbulence closure schemes used to solve the Navier-Stokes equations are not fully mature or are not always relevant for all scales due to the nonlinearity of these equations (Argyropoulos and Markatos, 2015;Novati et al., 2021). For mesoscale CTM models, the turbulent diffusion is mainly based on the gradient transport theory (K-theory) which is a first order turbulence closure. It defines the turbulence fluxes as analogous to molecular transport, assuming that the turbulence is a fluid cluster, which has the same properties as the molecular transport model, and a linear relationship between the gradient and the flux is thus obtained (Huang, 2019;Klimontovich, 1985). This local turbulence closure approach assumes that turbulence consists of only small eddies, causing diffusion-like transport. For example, the vertical kinematic flux ̅̅̅̅̅̅ of a pollutant can be modeled as being equal to an eddy thermal diffusivity K times the vertical gradient of mean concentrations ̅ .

̅̅̅̅̅̅ ̅

Also, the near-surface flow is complex process to simulate, the nature of turbulent transport is far from a simple linear relationship, it changes with the spatial scale of the atmospheric turbulence field and this notion of scale for turbulence is of major importance (He et al., 2019).

In mesoscale model, the grid size Δ is considerably larger than the energy containing turbulence scale l, the turbulent energy is clearly in the unresolvable sub-scale. Therefore, the turbulence only needs to be parameterized instead of directly resolved. Currently, the most mainstream

Chapter V 123/191
turbulence parameterization method is based on the first-order local closure scheme. In Chapter II, three parameterizations were analyzed based on the first-order local closure scheme, and the results show that this concise method is feasible. However, the high pollutants concentrations are frequently found under stable atmospheric and cold weather conditions, such conditions are characterized by a stably stratified lower atmosphere and very low turbulent diffusion [START_REF] Wolf-Grosse | Sensitivity of local air quality to the interplay between small-and large-scale circulations: a large-eddy simulation study[END_REF][START_REF] Zilitinkevich | Resistance and heat-transfer laws for stable and neutral planetary boundary layers: Old theory advanced and re-evaluated[END_REF]. In cities, traffic and local industrial emissions have a significant share of the local pollutants, those emissions can accumulate and have longer residence times near the ground level due to low transport and mixing [START_REF] Kurppa | Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation-Urban Planning Perspective[END_REF] which makes harder to provide an accurate prediction of pollution level and the early warning of pollution episode (PE). A way to improve the turbulence parameterization is to increase the order of the closure [START_REF] Stull | Turbulence Kinetic Energy, Stability and Scaling[END_REF].

In this study we use the eddy diffusion from the 1.5-order closure TKE embedded in the Weather Research and Forecasting (WRF) model as a new vertical diffusion coefficient (Kz) in the CTM CHIMERE to describe more realistic diffusion processes near the surface for urban pollution applications. Two schemes are defined: one as the initial K-theory diffusion scheme in CHIMERE (IKD) and one as the new eddy diffusion (NED) scheme from WRF respectively. A high spatial resolution simulation, which is conducted for a winter period from November 20 th to December 4 th , 2016 over three major cities (Paris, Lyon and Bordeaux) in France was performed with the WRF / CHIMERE suite. Based on our work presented in the previous chapters, the grid resolution of the models has strong influence on pollutants prediction [START_REF] Punger | The effect of grid resolution on estimates of the burden of ozone and fine particulate matter on premature mortality in the USA[END_REF]. The first step of the study is to understand the impact of vertical grid resolution on the NED simulation and find the finest vertical resolution for the following steps.

Second, examples of IKD and NED simulations are investigated for the different urban environments. The results are presented in terms of model performances between the configurations for the usual criteria pollutants. The final goal of this study is to propose a more advanced diffusion scheme to improve air quality simulations and forecast over urban and peri-urban area especially on the short-term pollution events.

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Model Description and Experiment Design

WRF and CHIMERE Model Description

As the first step of this experiment, the WRF model (Version 3.9.1) is used to provide the meteorological input data for the CTM. The initial and boundary conditions of WRF are issued from the Global Forecasting System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP), available at a 0.25° × 0.25° resolution at six-hourly time use a mass conservative approach to reproduce pollutant chemical transformations and transport. The initial vertical diffusion of CHIMERE defined as IKD in this study is based on first order closure scheme, it is mostly controlled by the PBL height. The horizontal diffusion is neglected in CHIMERE. The PBL height is directly read from WRF and capped at a minimal value of 20m to avoid unrealistic simulations. In CHIMERE, the vertical movement diffusion is calculated using the parameterization of Troen and Mahrt (1986) but without counter-gradient term in Equation (5.1):

( ) (5.1)
where k is the von Karman constant set to 0.41, z is the altitude, h is the boundary layer height, and is the vertical scale given by the similarity formulae, detailed explanation can be found in Chapter II. In this scheme, it can clearly see that the boundary layer height plays a crucial role in the vertical mixing, and have a direct impact on the mixing depth, height of maximum mixing and magnitude of mixing. Many studies indicate this first order closure K-theory is appropriate for mesoscale modeling particularly for neutral to weakly stable conditions. However, our previous studies have shown that during heavy pollution periods, there are huge differences in the boundary heights simulations according to different planetary boundary layer (PBL) schemes. Some of the PBL schemes cannot effectively simulate the height of the boundary layer during the pollution period, resulting in the simulation of pollutants concentrations several times the observation (Jiang et al., 2020). There are generally two ways to improve the mesoscale vertical mixing. The first is to use a higher-order local closure scheme to replace the first-order local closure. Second, the unknown quantity on the grid point can be determined by the known quantity on the grid point farther away in the vertical direction, which is called non-local closure scheme (Warner, 2010).

In WRF, the commonly used 1.5 order local mixing PBL schemes including

Mellor-Yamada-Janjic (MYJ), Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2) and Boulac scheme, the nonlocal mixing PBL schemes including Yonsei University (YSU) and asymmetric convective model V2 (ACM2) PBL schemes. The TKE order-1.5 model was

Results and Discussions

Impact of the first layer height on NED modelling

Three vertical grids are tested to study the sensitivity and impact of the first layer height performed with the WRF and CHIMERE, from 27 th November to 4 th December 2016 over Paris region.

The aim is to quantify the variability in the surface pollutants concentrations results by implementing the NED scheme in the mesoscale CTM which would be more representative to the simulation of air flow and pollutant dispersion in urban atmosphere particularly on the PE.

Figure5.4 gives the vertical diffusion coefficient in urban and rural area. The Kz profiles show different shapes and magnitudes between H4, H12 and H40. The values of diffusivity are relatively poor at low levels from H4 scheme and do not exceed 0.12m 2 /s on the first layer in both urban and rural area. H12 gives 0.71m 2 /s and 0.25m 2 /s in urban and rural area with is approximately 7 and 2 times than H4. H40 reaches the maximum diffusion rate since the first layer and decreasing with the increasing altitudes. For all schemes and regions, the most important differences are in the low layers, showing that the point close to the surface generates a complex mixing under different first layer height.

Figure5.4 Vertical profiles of the CHIMERE Kz from NED scheme extracted at the FR04143 station.

Taking NO 2 as the reference pollutant, Figure5.5 gives the surface NO 2 mass concentrations in urban and rural background station between observation and three sensitivity simulations. It is noteworthy that the models capture well the evolution of NO 2 concentrations over the urban and rural stations, these first results show that implementing a more advanced scheme is feasible. H4 strongly overestimates NO 2 concentrations for the whole period in the urban regions, the reason is the strong underestimation of Kz at lower levels. Table5.1 lists the correlation (R), mean bias (MB) and root mean square error (RMSE) of each sensitivity studies. H12 displays a more reasonable behavior of NO 2 than the two other grid and will be used as the model configuration of vertical grid resolution for the following steps. This sensitivity test indicates the first layer height is crucial on TKE parameterization, an extra fine first layer height at 4m does not produce better simulation results.

Figure5.5 Time series between observed and modelled NO 2 mass concentrations (µg/m 3 ) from NED scheme in urban and rural background stations in Paris region.

Table5 Road traffic emission is the largest contribution to NO 2 concentrations in urban areas [START_REF] Dragomir | Modeling results of atmospheric dispersion of NO 2 in an urban area using METI-LIS and comparison with coincident mobile DOAS measurements[END_REF][START_REF] Lee | Land use regression models for estimating individual NOx and NO2 exposures in a metropolis with a high density of traffic roads and population[END_REF][START_REF] Palmgren | Effects of reduction of NOx on the NO2 levels in urban streets[END_REF]. The mean first layer NO 2 and O 3 mass concentrations and Kz over the period are shown in Figure5.6. The results show that the absolute values are different between the IKD and NED scheme, the regional patterns are similar for the Kz and the two pollutants. It is clear that the results over urban areas exhibit the role NO 2 emissions on NO 2 concentrations. The Kz simulated by the NED method is significantly lower than the IKD scheme in the entire region. The average surface Kz from NED scheme is 1.13m 2 /s in the urban region with is approximately half of the IKD scheme (2.50m 2 /s), this value is similar to our plan of dividing Kz by 2 in urban areas since the Version-2017 of CHIMERE. Consequently, the NED scheme runs generates remarkably higher surface NO 2 concentration by 6.93µg/m 3 on average in urban area. The spatial differences for O 3 mass concentration between IKD scheme and NED scheme are more substantial than for NO 2 . The O 3 and NO 2 maps show that the area with maximum NO 2 increase coincide well with that of sharp O 3 reduction due to the titration effect. [START_REF] Yao | The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> Chapter V 159/191 directly emitted from vehicles[END_REF] indicated that the contribution of direct NO 2 emissions to the atmospheric NO 2 concentration is low, the NO 2 concentrations mainly comes from the oxidation of NO. The chemical reaction involving NO with O 3 plays an important role in the formation of NO 2 [START_REF] Song | Relationships among the springtime ground-level NOx, O3 and NO3 in the vicinity of highways in the US East Coast[END_REF], even in winter. Relatively high NO 2 concentrations were observed in all four urban background stations, with a mean concentration of 53.45 µg/m 3 ; 50.05 µg/m 3 ; 52.47 µg/m 3 ; 49.66 µg/m 3 in FR04002; FR04034; FR04143; FR04156 station, respectively. Table5.2 lists the performances of NO 2 simulation for both schemes with their average mass concentration (Avg); correlation (R); mean bias (MB) and root mean square error (RMSE) at each station. The results show that the period is well modelled by the IKD scheme with the correlation from 0.62 to 0.74 among stations. However, this scheme seems underestimated NO 2 concentration in all stations, this is mainly because of the underestimation during the PE. The performance of NED scheme is very satisfactory and improves NO 2 simulations for most of stations. Taking the relative changes in RMSE as the evaluation criteria, the largest improvements occur for FR04034 station (the improvement is of 19.22%). The averaged time series and statistics for the urban background stations are summarized in Figure S5.2 and Table S5.1. The two schemes capture quite well the evolution of NO 2 concentration over the urban region, the R and MB are 0.74 and -10.40µg/m 3 for IKD scheme and 0.80 and -3.80 µg/m 3 for NED scheme with the improvement is of 18.77% in the urban area. It is noteworthy that both schemes give a better performance in the city center station (FR40143) than others, it may indicates categories of urban area are not enough detailed in the model, which might help assessing the urban categories and roughness length for the model in the urban area. The performances of O 3 simulation from IKD and NED schemes are quite different according to their performances of NO 2 simulation. The IKD scheme gives a clearly better performance in the first three days of the period. Subsequently, both schemes significantly overestimate the O 3 concentration at all urban background stations until 27 th November. Previous researches reported that surface O 3 concentrations in wintertime are highly correlated to reduced photochemical activity (Chen et al., 2020;[START_REF] Peshin | Spatio-temporal variation of air pollutants and the impact of anthropogenic effects on the photochemical buildup of ozone across Delhi-NCR[END_REF]. Therefore, the overestimation of O 3 may due to underestimation of cloud thickness in this period. Table5.3 provides an overall evaluation for IKD and NED schemes for the urban stations. O 3 concentrations are on average overestimated particularly for the IKD scheme. However, the time series shows that the O 3 concentration is strongly overestimated in some days, therefore the MB cannot well represent the simulation accuracy of two schemes for O 3 . Unlike the results of NO 2 simulation, the IKD scheme gives Chapter V 134/191 a better correlation and RMSE in all stations than NED scheme. The NED scheme resulted in 4.05%, 9.12%, 1.27% and 13.86% drops in O 3 simulation accuracy in four urban stations. In order to deeply investigate the differences between the schemes, the diurnal cycles are calculated for Kz, NO 2 and O 3 for the corresponding surface observations at the urban background stations over the Paris region. Figure5.8(a) illustrates the mean diurnal Kz on urban regions. The diurnal variation of the surface Kz throughout the schemes shows similarities, Kz remains flat in the morning and nighttime and a peak is simulated around 12:00. The average Kz on the unstable condition from IKD scheme is approximately 1.7 times than NED scheme and approximately three times higher on the stable condition. The bimodal distribution of the surface NO 2 concentration are observed and simulated during this period, with the morning peak around 8:00-9:00 and the evening peak around 18:00-19:00. Both schemes capture the fluctuations quite well, but IKD scheme strongly underestimates NO 2 concentrations in the early morning. The results of the Chapter II prove that the IKD scheme overestimates the vertical diffusion rate in urban areas. This section further demonstrates that the overestimation of vertical diffusion under stable conditions is higher than under unstable conditions in IKD. The NED scheme simulation overestimates NO 2 concentration at nighttime, the reason should be the overestimation of Kz drop after 14:00.

PM2.5 and PM10 Simulations over Paris region

In Figure5.10, surface PM2.5 and PM10 concentrations are illustrated for IKD and NED schemes. The surface concentration fields show a gradient between the urbanized area surroundings in both schemes but not as strong as for NO 2 surface distributions, this result is expected because PM2.5 and PM10 is much more influenced by regional transport and peri-urban and rural emissions from biomass burning. The PM2.5 and PM10 surface concentrations from NED scheme is approximately 4.4µg/m 3 and 4.2µg/m 3 higher on average than the IKD scheme.

Figure5.10 Maps of the mean surface PM2.5 and PM10 mass concentration (µg/m 3 ); over the period from 20 th November to 4 th December. We also notice that the underestimation of PM10 is obviously higher than PM2.5 during the PE. Table5.7 reports the ratio between PM2.5 and PM10 during the whole period (WP) and PE in urban, suburban and rural areas. The location of suburban and rural PM2.5 monitoring stations are not completely matched with PM10 monitoring stations. Thus, the ratio PM2.5/PM10 for suburban and rural sites are given for guidance, we focus here on urban sites only. There is no evident variation of this ratio on the PE for both observation and simulations, The IKD and NED schemes are also applied in Bordeaux and Lyon areas in order to understand whether the NED scheme can be used in other areas. Lyon is in the southeast of France and is located in the Rhone valley where the wind is meridional and often strong particularly in winter and is the second largest city in France. Bordeaux is close to the sea that is often influenced by oceanic flows and located in the southwest part of France. The period of interest remains the same from 20 th November to 4 th December.

Ground Meteorological Variables

Here, we compared observation of 2 meters temperature (T2) and 10 meters wind speed (W10) from four meteorological observation sites (three in Lyon and one in Bordeaux) to the simulations from WRF. The comparison is of hourly average values from observation sites with hourly average values from the nearest of each site location. Figure5.12 presents the maps of averaged T2 and W10 simulated in Lyon and Bordeaux over the period. Both regions show a UHI effect in the urban region, which gives positive bias of up to 3°C over the period in Lyon and 2.5°C in Bordeaux respectively compared to the rural areas. The maps of W10 presents a higher surface wind speed in rural areas than in urban regions with a negative bias for 3.2m/s in Lyon and 3.5m/s in Bordeaux respectively. The City of Lyon is formed by the core city of Lyon and its 58 suburban municipalities, so the UHI effect is more widespread than Bordeaux.

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Figure5.12 Maps of the mean 2m temperature (°C ) and 10m wind speed (m/s) over the period from 27 th November to 4 th December.

Figure5.13 presents the time series of hourly-averaged T2 and W10 for the whole period. T2 overall shows a decreased tendency throughout the period and the model effectively captures this trend. Compared with the observations, the model tends to overestimate the T2 for all stations, with a positive bias for 2.36°C in Lyon and 2.16°C in Bordeaux during this period and a positive bias for 3.09°C in Lyon and 2.70°C in Bordeaux on the PE. The diurnal differences for W10 are relatively low in Lyon, the models overestimate the wind speed in Lyon, with a positive bias for 2.76m/s for the whole period and 0.77m/s on the PE, the main bias of W10 simulation come from the overestimation from the first four days of the period.

In Bordeaux, W10 simulation is closed to the observation, with a negative bias for -0.48m/s for the whole period and 0.33m/s on the PE. The mean first layer Kz and NO 2 mass concentrations for Lyon and Bordeaux domains over the period are displayed in Figure5.14 and Figure5.15 respectively. Both domains present similar regional distribution when compared to the Paris domain. The NO 2 concentrations are higher within the urban area and become patchier in the suburban and rural area, these concentrations are higher near the traffic routes. Paris. This demonstrates that the PE in Lyon and Bordeaux were not primarily caused by local sources. In Lyon, it seems that there are two slight NO 2 pollution episodes during this period.

One from 22 nd November to 26 th November and another from 30 th November to 3 rd December.

Overall, both schemes capture the majority of fluctuations quite well in both two regions. In particular, the NED scheme almost perfectly captures the first peak of NO 2, the 22 nd December. However, despite the IKD scheme captures the fluctuations of NO 2 , the second light peak in Lyon since the nighttime of 30 th November in the observations and the IKD scheme underestimated NO 2 concentration at this period.

Table5.8 reports a detailed evaluation of NO 2 simulation for both schemes at each urban background station. Similarly, to the focus in Paris, the IKD scheme underestimates NO 2 concentrations at all urban sites especially on the period of 30 th November to 3 rd December.

Taking the RSME as the criterion, the use of the NED scheme improves NO 2 simulation in every urban site especially in the three urban sites in the Lyon region with an average improvement of 24.51%. In general, the model has a better performance for NO 2 simulation in Bordeaux than Lyon, that because the terrain of Lyon is more complex than in Bordeaux, the city is close to the Alps in a hilly region with sharp terrain gradients and the distribution of urban land use that is more scattered than Bordeaux.

Figure5 since the 1 st December. Table5.9 reports that NED gives a better performance in both suburban sites of Lyon and rural sites of Bordeaux, with an improvement of 13.84% and 3.26% respectively. Even the performance of the model in suburban of Lyon cannot be compared with the rural of Bordeaux directly, the model should give better performances in the suburban and rural area in Bordeaux than Lyon.

Conclusions

The flow characteristics and dispersion mechanisms of pollutants are important for air quality predictions particularly over urban areas. However, the representation of turbulent diffusion in mesoscale models remains a challenge especially for transport chemistry models. This study used a 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme from WRF defined as NED on the urban air quality context to assess consequences of surface-level emission of nitrogen dioxide (NO 2 ) and particulate matter (PM2. The study in Paris indicates both two schemes can capture the evolution of the NO 2 , O 3 , PM2.5 and PM10 concentrations in urban, suburban, and rural areas even if some constitutive species are underestimated on the pollution episode (PE). The NED scheme shows a better ability on PE prediction and displays a better performance in urban areas for NO 2 , PM2.5 and PM10 simulation with an average improvement at 18.77%, 13.47% and 19.08% respectively.

In suburban areas, the NED scheme improves the simulation, but the degree of improvement was not as strong as in the urban areas. The NED scheme is not performing as well as IKD in Bordeaux than IKD scheme, with an average improvement at 13.84% and 3.26% respectively.

However, PM simulations in Lyon and Bordeaux indicate that a strong underestimation of biomass burning emissions in wintertime could be the main reasons. The NED scheme gradually improves the air quality simulation from rural to urban areas. Although the overall performance of the proposed method is better than initial model configuration, the NED scheme still slightly overestimates the concentration of major pollutants on RD. The underestimation of Kz of NED is more pronounced in the nighttime than the daytime. Further improvements are necessary particularly under stables boundary conditions. In general, the 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme is an effective way to improve the CTM ability particularly under low stagnant conditions. It can be expected further improvement of the model ability through a higher-order turbulent closure calculation.

Conclusions and Perspectives

 The first order K-theory is an effective way to reproduce the dispersion of pollutants in mesoscale chemistry transport model in most of time. However, this method sometimes lacks the ability to predict vertical mixing under a very low turbulent diffusion.

 The urban canopy and boundary layer height plays a crucial role in the mesoscale chemistry transport modelling simulation, especially for the boundary layer height, it plays a key role in the early warning of pollution episode.

 Geographical condition is also important in regional transport and dispersion of pollutants. Pollutant's dispersion processes over the valley-basin city are much more complicated than over flat areas.

 Our study indicates the 1.5 order TKE-based eddy diffusivity closure scheme improved the CTM in every urban site. A way to improve the turbulence parameterization is to increase the order of the closure.

With an increasing number of humans now living in urban areas, there are needs of investigating the role of urbanization on air pollution, local meteorology and climate change.

With the increasing computing capacities, numerical tools have become attractive tools for simulating transport and dispersion of airborne materials in the urban canopy. The present thesis work uses mesoscale models for meteorological and air quality simulations, representative of background air quality in urban region and its surroundings. The main goal of the thesis is to evaluate and improve the ability of models to simulate and forecast air quality and in France over urban regions.

Initially, three vertical diffusion schemes which have been used in multiple mesoscale chemical transport models (CTM) were tested over a one-year simulation covering the whole France. Those schemes are all based on first order local closure scheme, but the expressions differ. All three schemes present a fair reproduction of pollutant concentrations both in urban and rural areas, indicating it is an effective way to reproduce the dispersion of pollutants in chemistry transport modelling. However, the differences between test results are weak.

Therefore, it cannot be expected to significantly improve the vertical mixing in CTM under the first order closure scheme.

In the second part of the study, the impact of physical parameterizations was evaluated through couple the CTM model CHIMERE with the Weather Research and Forecasting (WRF) mesoscale model. Three canopy schemes, including a single layer urban canopy scheme (UCM); a multiple layer urban canopy scheme (BEP) and a reference case (Bulk). In the last part of the study, a 1.5-order turbulence-resolve scheme defined as NED has been applied in the CHIMERE model on the urban air quality context to assess consequences of low-level emission of pollutants in Paris, Lyon and Bordeaux. The performances of NED and initial Kz diffusion scheme (IKD) were evaluated against ground observations both temporally and spatially. The study in Paris shows both two schemes can capture the evolution of the NO 2 , O 3, PM2.5 and PM10 concentrations in urban, suburban, and rural areas even if some constitutive species are underestimated on the pollution episode (PE). Taking the relative change in % of the RMSE as the evaluation criteria, the NED scheme improved the ability of models to assess air quality with an average improvement at 18.77%, 13.47% and 19.08% of NO 2 , PM2.5 and PM10 respectively. However, the IKD scheme reproduces the O 3 more effectively than NED. In suburban areas, NED scheme improved the simulation, but the degree of improvement was not as strong as in the urban areas. In recent years, due to the improvement of computing power, the interdisciplinary field of machine learning-statistics and computer science has flourished. At present, the application of Artificial Intelligence (AI) techniques to air quality simulation has been initially applied but is still in the exploratory stage. For example, use wavelet transform to split the pollutant time series into more regular sub-sequences, then use the artificial neural network to train independent models for them to further improve the accuracy. Or using AI techniques could be used to calculate higher-order turbulent kinetic energy makes it possible to apply direct numerical simulation to mesoscale models. How to combine CTM and AI techniques for air quality prediction will be a very active field in the future to allow fast high-resolution simulations at urban scale to monitor air quality. At last, let us remind that using neural network is complementary of developing more and more complex physical models with more layers and accurate urban schemes. They are good instruments to simplify complex models by developping surrogate associated models for operational uses at high resolution (by catching the main patterns) and offering the possibility to develop more and more sophisticated deterministic models representing the "real world".

List of Figures

Figure1.1 Typical sources, formation, removal pathways and size distributions of atmospheric aerosol.

Figure1.2 Direct, semi-direct and indirect effects of aerosols on climate. Figure1.5 Emission trends in the EU28 on the basis of fuel sold.

Figure1.6 Urban heat island in cities.

Figure2.1 General principle of the air quality simulation system Figure2.2 The main linkages between megacities, air quality and climate. Figure2.5 Sensitivity test of vertical profile of kz (m2/s) in stable and unstable conditions.

Figure2.6 Vertical profiles of kz (m 2 /s); NO 2 (µg/m 3 ); O 3 (µg/m 3 ) PM 2.5 (µg/m 3 ) and PM 10 (µg/m 3 ) over the France domain. The profiles are averaged from January 1 st to December 31 st , 2016.

Figure2.7 Maps of the seasonal mean vertical diffusion coefficient (m 2 /s) over the France domain.

Figure2.7 Seasonal changes between observed and modelled NO 2 , PM 2.5 and PM 10 mass concentrations (µg/m 3 ) in France domain.

Figure2.9 Time series between observed and modelled NO 2 , PM 2.5 and PM 10 mass concentrations (µg/m 3 ) in Urban and Rural areas. Figure3.2 Four nested domains-from d01, the largest one, at a low resolution to d04, the finest one, at a high resolution.

Figure3.3 Maps of the meteorological and air quality monitoring stations.

Figure3.4 Time series of 2m temperature and 10m wind speed at Sirta station.

Figure3.5 Time series of 2m temperature and 10m wind speed at Montsouris station.

Figure3.6 Maps of the mean 2m temperature (°C) over the period from 27 th November to 4 th December Figure3.7 Maps of the mean surface heat flux (W m -2 ) over the period from 27 th November to 4 th December.

Figure3.8 Time series of 2m temperature (°C) between observations and different urban schemes at Montsouris, Roissy, and Sirta stations.

Figure3.9 Maps of the mean 10m wind speed (m s -1 ) over the period from 27 th November to 4 th December.

Figure3.10 Comparison of the time series of 10 m wind speed (m s -1 ) between the observations and different urban schemes at Montsouris, Roissy and Sirta stations.

Figure3.11 The differences in the daily averaged NO 2 (μg m -3 ), PM 2.5 (μg m -3 ), and PM 10 (μg m -3 ) between different schemes from 27 th November to 4 th December 2016.

Figure3.12 Time series of NO 2 mass concentrations (µg m -3 ). No1, 2, 3, 4 represent station FR04002, FR04034, FR04143, and FR04156, respectively. Figure3.17 Time series between the observed and modelled W10: (a) and (b) represent Roissy and Sirta station, respectively; 1, 2, and 3 represent each PBL scheme coupled with each urban canopy scheme.

Figure3.18 Time series of NO 2 mass concentrations: No 1, 2, 3, and 4 represent station FR04002, FR04034, FR04143, and FR04156, respectively.

Figure3.19 Liner fit between the observed and modelled NO 2.

Figure3.20 Time series of 2m temperature (°C) between observations and different urban schemes at Montsouris, Roissy, and Sirta stations. Figure4.7 Time series between the observed and modelled NO 2 , PM 2.5 , PM 10 (μg/m 3 ).

Figure4.8 Time series of bias between L12 to L4 (L12-L4) and L40 to L4 (L40-L4).

Figure5.1 Schematic of a turbulent-energy spectrum. l is the scale of the energy-containing turbulence, Δ is the horizontal gird size; ɸ is turbulent energy, and λ is wavelength (Warner, 2011). Figure5.8 Time series between observed and modelled NO 2 and O 3 mass concentrations (µg m -3 ) suburban background stations and rural background stations.

Figure5.9 Maps of the mean surface PM 2.5 and PM 10 mass concentration (µg m -3 ); over the period from 20 th November to 4 th December.

Figure5.10 Time series between observed and modelled PM 2.5 and PM 10 mass concentrations (µg m -3 ) in urban; suburban and rural background stations. Urban background stations: FR04002; FR04034; FR04143 and FR04156. Suburban background stations: FR04024 and FR04181 for PM 2.5 simulation and observation; FR04181 and FR04319 for PM 10 simulation and observation. Rural background stations: FR04048 for PM 2.5 simulation and observation;

FR04158 PM 10 simulation and observation.

Figure5.11 Maps of the mean 2m temperature (°C ) and 10m wind speed (m s -1 ) over the period from 27 th November to 4 th December.

Figure5.12 Comparison of the time series of 2m temperature (°C ) and 10 m wind speed (m s -1 ) between the observations and different urban schemes at Bron, Exupery, Brindas and Merignac stations. The Bron, Exupery and Brindas stations are located in Lyon region and Merignac station is located in Bordeaux region.

Figure5.13 Maps of the mean vertical diffusion coefficient (m 2 s -1 ); over the period from 20 th November to 4 th December. The first line: results from Lyon domain; the second line: results from Bordeaux domain.

Figure5.14 Maps of the mean surface NO 2 mass concentration (µg m -3 ); over the period from 20 th November to 4 th December. The first line: results from Lyon domain; the second line:

results from Bordeaux domain. List.S1. Name list of WRF model &time_control run_days = 0, run_hours = 24, run_minutes = 0, run_seconds = 0, start_year = _Y1_, _Y1_, _Y1_,_Y1_, start_month = _M1_, _M1_, _M1_,_M1_, start_day = _D1_, _D1_, _D1_,_D1_, start_hour = 00, 00, 00, 00, start_minute = 00, 00, 00, 00, start_second = 00, 00, 00, 00, end_year = _Y2_, _Y2_, _Y2_,_Y2_, end_month = _M2_, _M2_, _M2_,_M2_, end_day = _D2_, _D2_, _D2_,_D2_, end_hour = 00, 00, 00, 00, end_minute = 00, 00, 00, 00, end_second = 00, 00, 00, 00, interval_seconds = 21600 input_from_file = .true.,.true.,. = 1., 0.9995, 0.999, 0.9985, 0.9975, 0.996230185, 0.9949736, 0.993716955, 0.992334723, 0.990814209, 0.989141703, 0.987301886, 0.98527813, 0.983051956, 0.980603218, 0.977909565, 0.974946558, 0.971687257, 0.968101978, 0.964158237, 0.959820092, 0.955048144, 0.949799001, 0.94402492, 0.937673509, 0.930686891, 0.923001587, 0.914547801, 0.905248582, 0.895019472, 0.883767486, 0.871390283, 0.857775331, 0.842798889, 0.826324821, 0.80820334, 0.788269699, 0.7663427, 0.742223024, 0.715691328, 0.68650645, 0.65440315, 0.619089544, 0.580244482, 0.537514985, 0.49051252, 0.438809812, 0.381936818, 0.319376528, 0.250560224, 0.17486228, 0.0915945247, 0.0000, eta_levels for H12 = 1., 0.9985, 0.9975, 0.996230185, 0.9949736, 0.993716955, 0.992334723, 0.990814209, 0.989141703, 0.987301886, 0.98527813, 0.983051956, 0.980603218, 0.977909565, 0.974946558, 0.971687257, 0.968101978, 0.964158237, 0.959820092, 0.955048144, 0.949799001, 0.94402492, 0.937673509, Annex B 186/191 0.930686891, 0.923001587, 0.914547801, 0.905248582, 0.895019472, 0.883767486, 0.871390283, 0.857775331, 0.842798889, 0.826324821, 0.80820334, 0.788269699, 0.7663427, 0.742223024, 0.715691328, 0.68650645, 0.65440315, 0.619089544, 0.580244482, 0.537514985, 0.49051252, 0.438809812, 0.381936818, 0.319376528, 0.250560224, 0.17486228, 0.0915945247, 0.0000, eta_levels for H40 = 1., 0.9949736, 0.993716955, 0.992334723, 0.990814209, 0.989141703, 0.987301886, 0.98527813, 0.983051956, 0.980603218, 0.977909565, 0.974946558, 0.971687257, 0.968101978, 0.964158237, 0.959820092, 0.955048144, 0.949799001, 0.94402492, 0.937673509, 0.930686891, 0.923001587, 0.914547801, 0.905248582, 0.895019472, 0.883767486, 0.871390283, 0.857775331, 0.842798889, 0.826324821, 0.80820334, 0.788269699, 0.7663427, 0.742223024, 0.715691328, 0.68650645, 0.65440315, 0.619089544, 0.580244482, 0.537514985, 0.49051252, 0.438809812, 0.381936818, 0.319376528, 0.250560224, 0.17486228

(

  EPA) developed the third-generation air quality simulation platform Models-3 based on the concept of "one atmosphere" (Byun and Schere, 2006a). At present, the most widely used are the third-generation comprehensive CTM in clouding WRF-Chem, the Comprehensive Air-quality Model with extensions (CAMx), Community Multiscale Air Quality Model (CMAQ), CHIMERE, etc.

  and exceedances of limit values for criteria pollutants. Patterns of urban air pollution are rather variable and spatially heterogeneous. Turbulence plays an important role on the vertical mixing of physical parameters and pollutants.. Understanding the impact of multiple parameterization schemes on air quality simulation can help improve model configuration to address local areas.The third generation of CTM system generally has three basic modules: a meteorological process module (input information from mesoscale meteorological models), emission inventory pre-processor and the core air quality simulation module. Studies indicate that the physics parameterizations play the key role in both meteorological and air quality simulation particularly in urban areas. The buildings and cement pavement form the urban canopy layers and surface roughness, therefore change thermal and dynamic characteristics of the surface layer. These changes will significantly influence the surface heat accumulation that has a negative effect of the planetary boundary layer (PBL) height and surface wind speed which affect the transport and dispersion of pollutants. The PBL height is a key factor on the formation and evolution of pollutants. A low boundary layer height is regarded as a major meteorological process for haze formation. Thus, a smart selection of physics parameterizations plays a crucial role in urban air quality prediction.With the development of computer performances, high resolution meteorological and air quality simulations become possible. However, studies demonstrate that the higher resolution does not represent better simulation results, horizontal resolution below 1km is not necessary.Meanwhile, the building clusters modified surface roughness and zero-plane displacement height, an overestimated vertical resolution can cause unreasonable wind flows near surface.The K-theory shows certain advantages in dealing with the dispersion of air pollution at the mesoscale. It can use the observed wind speed profile data to obtain the concentration distribution of pollutants without assuming a certain form of distribution. However, high pollutants concentrations are frequently found under stable and cold weather conditions, such conditions are characterized by a stratified lower atmosphere and weak turbulent diffusion. In cities, traffic, residential and local industrial emissions have a major share of the pollutants; those emissions can accumulate and have longer residence times near the ground level due to the inefficient transport and mixing. The transient turbulent flow plays an important role on the transportation and deposition of pollutants, thus, an advanced vertical eddy coefficient may helpful to provide an accurate prediction of pollution during the short-term episode. It is noteworthy that meteorological sciences have focused on extreme events to forecast heavy precipitations, thunderstorms, high wind speed conditions while air quality is mainly driven

  1.1) This general equation describes the chemical reaction, transport and diffusion, deposition and emission of pollutants. With ρ the concentration of gas; t the time; u, v, w the component of wind speed in x, y, z direction; K x , K y , Kz the horizontal and vertical diffusion coefficient; S the source and sink of pollutants; R chemical reaction term, determined by the chemical reaction mechanism of pollutants in the atmosphere. Figure2.3 gives an example of mass conservation, taking the x direction as an example, the input and output wind speeds are u 1 Chapter II 39/191 and u 2 , respectively, the gas concentrations at the left and right boundaries of the cube are ρ 1 and ρ 2 . Then, the flux densities of the input and output cubes are F x1 = u 1 ρ 1 and F x2 = u 2 ρ 2 , respectively.

Figure2. 7

 7 Figure2.7 displays averaged vertical profiles of Kz, NO 2 , O 3 , PM2.5 and PM10 for the year 2016. The Kz profiles show similar shapes from surface to the top, with an upward trend within 13 levels (approximately 550m). Table2.1 lists detail information of Kz for the first 10 levels, the Kz1 displays lowest Kz at all levels, Kz2 and Kz3 begin to reverse from the seventh level (approximately 82m). The vertical NO 2 , PM2.5 and PM10 concentration profiles show that the increasing mass concentrations from surface to the top because of the anthropogenic emissions, highest concentrations are simulated in the Kz1 at low levels. The average RB between Kz1 and Kz2, Kz1 and Kz3 are 2.2% and 7.3% approximately. Kz2 provides stronger diffusion rate than Kz3 in the first layers, however, the pollutants concentrations at low levels are higher in Kz2. This indicates that surface pollutants concentrations are not only depend on the corresponding Kz. The transport also plays an important role in the low levels pollutants concentrations. The vertical profile of O 3 concentrations are different than for other pollutants, it shows lower concentrations in the surface than for upper layers. The vertical O 3 concentrations are different compared to other species, with the highest concentration in Kz3 scheme at surface. The reason is the fast titration by NO x and deposition processes close to the surface.

  Kz3 in winter respectively. Compare with the model performances of PM simulations, the three schemes display higher biases for NO 2 simulations in spring, autumn and winter. NO 2 mainly comes from vehicle exhaust emission in cities, it can indicate the model may overestimate vertical diffusion rate in urban region. The model displays relatively better performance in PM2.5 simulation, with slight negative biases in summer and positive biases in the rest of seasons. However, three schemes underestimated PM10 concentrations in every season. As shows in Table2.2, the observed PM10 concentrations are approximately the double of the simulation in summer. This could indicate the underestimation of coarse particles in the emission inventory.

  schemes, the bias is approximately 25.3%. This result indicates the models could have a large overestimation of fine mode emissions or that coarse emissions are lacking. All three schemes in rural areas display a quite good reproduction of the evolution of pollutant concentrations and better capture the fluctuations of pollutants in the wintertime than urban areas. The model underestimate PM10 concentration particularly in summertime. Table2.3 provides an overall evaluation of three schemes in urban and rural areas including R, MB and RMSE. In general, the models present a better performance in rural than the model performances in urban areas.Concentrations of NO 2 and PM10 are stronger underestimated in urban areas than for rural areas. This indicates the three schemes tend to overestimate the local vertical diffusivity in urban region compared to the rural areas. The value of exponent p is considered as 2 (the -square‖ in the equation) in equation (1.6) and (1.9). The range of this value has been

Figure2. 11

 11 Figure2.11 Diurnal cycles for surface concentrations (µg/m 3 ) of NO 2 , PM2.5 and PM10 in the year 2016

Figure3. 1

 1 Figure3.1 The structure diagram of UCM (left) and BEP (right) urban canopy scheme. Ta: Atmospheric temperature at Za meters height; Tg: Pavement temperature; H : Sensible heat exchange; Tr: Temperature of the roof; Tw: Temperature of the wall; Tg: Temperature of the street; Ts: Atmospheric temperature at Zs meters height; Ha: Sensible heat flux from street canyon to atmosphere; Hw: Sensible heat flux from wall to street canyon; Hg: Sensible heat fiux from road to street canyon; Hr: Sensible heat fiux from roof to atmosphere.

Figure3. 2

 2 Figure3.2 Four nested domains-from d01, the largest one, at a low resolution to d04, the finest one, at a high resolution. Two types of observation data are used in the present study: (i) three meteorological observation stations operated by METEO FRANCE and SIRTA were used to evaluate the simulated surface meteorological variables from the model; and (ii) the hourly surface concentrations of criteria pollutants (NO 2 , PM2.5, and PM10) issued from AIRPARIF, the official Paris air quality network. The location of the three meteorological observation stations (Montsouris, Roissy, Sirta) and the four air quality monitoring stations can be found in Figure3.3; they are urban or sub-urban background stations. The FR04143 air quality monitoring station is located in the city center. -Roissy‖ is a synoptic station of the Paris

  other schemes during the PE. This sensitivity test indicates that more realistic urban morphologies are needed to improve the model performance, because the parameters used in the urban canopy scheme (e.g., albedo, heat capacity, and roughness length) strongly influence the surface energy balance and heat absorption.

  Figure3.14 Vertical profiles of the NO 2 and PM2.5 mass concentration (µg/m 3 ) at the FR04143 station. The profiles are extracted at 06:00 and 22:00 UTC for both Regular Days (RD) and the Particulate Episode (PE). Columns (a), (b), (c), and (d) represent the vertical mass NO 2 and PM2.5 concentration of at 28th November 06:00, 28 th November 22:00, 30th November 06:00, and 30 th November 22:00 UTC, respectively.

  Figure3.17 Time series between the observed and modelled W10: (a) and (b) represent Roissy and Sirta station, respectively; 1, 2, and 3 represent each PBL scheme coupled with each urban canopy scheme.

Figure3. 19

 19 Figure3.19 Liner fit between the observed and modelled NO 2.

  Modeled mean 10m wind speed across the three stations is shown in Figure3.21. The results show Noah-mp almost had no effect in the W10. The possible reason is the Noah-mp improve the vegetation canopy from the surface which could be more efficient in vegetated areas through plant stomata and root uptakes of soil water then better account for the surface energy balance. The surface wind speed in urban region mainly influenced by the city clusters and

  surface temperature and fluxes. But for the air quality simulation and forecast, Urban canopy and PBL schemes may plays a more important role than LSM.

Figure3. 22

 22 Figure3.22 Time series of NO 2 and PM2.5 mass concentrations: No 1,[START_REF] Yao | The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> Chapter V 159/191 directly emitted from vehicles[END_REF] 3, and 4 represent station FR04002, FR04034, FR04143, and FR04156, respectively.

  schemes (MYJ-Bulk, YSU-Bulk, MYJ-UCM, YSU-UCM, MYJ-BEP, and Boulac-BEP) from the Weather Research and Forecasting (WRF Version 3.9.1) mesoscale model on the simulations of meteorological variables and predicted pollutant concentrations issued from the

  L12 et L40) ont é té é tudié es : (i) L4 : 53 couches verticales dans WRF et 20 niveaux verticaux dans CHIMERE sont dé finis, la hauteur de la premiè re couche est d'environ 4m ; (ii) L12 : 51 couches verticales dans WRF et 20 niveaux verticaux dans CHIMERE, la hauteur de la premiè re couche est d'environ 12m ; (iii) L40 : 36 couches verticales dans WRF et 9 niveaux verticaux dans CHIMERE, la hauteur de la premiè re couche est d'environ 40m. Une simulation sur quinze jours en hiver incluant un é pisode de forte pollution (PE) de trois jours illustre l'impact de la ré solution de la grille verticale. La hauteur de la premiè re couche a un impact relativement faible sur la simulation mé té orologique et les principales diffé rences de variables mé té orologiques ont é té observé es la nuit. Cependant, une amé lioration significative de la modé lisation du transport des polluants a é té constaté e avec des ré solutions verticales raffinées et la hauteur de la première couche. Ainsi, l'amélioration moyenne du cas L4 par rapport à L12 et L40 en termes de RMSE est de 23,26% et 25,09% sur les jours ré guliers (RD) sans é pisodes de pollution et 62% et 129% pendant PE respectivement. En gé né ral, la capacité du modè le à faible ré solution verticale à reproduire correctement les concentrations de polluants est raisonnable sur les RD ou les é pisodes de pollution lé gè re, alors qu'il ne parvient pas à reproduire correctement les concentrations de polluants pour les situations de forte pollution. Les performances ont é té amé lioré es avec une ré solution verticale affiné e, en particulier avec une hauteur de premiè re couche trè s fine.

  CHIMERE and WRF individually, and the configurations of the first layer height of the two models remain the same:  L4: 53 vertical layers are defined in WRF and 20 vertical layers are defined in CHIMERE. The first layer of the two model is at 999.5 hPa (approximately 4m) up to over 500hPa.  L12: 51 vertical layers are defined in WRF and 20 vertical layers are defined in CHIMERE, with the first layer at 998.5 hPa (approximately 12m) up to over 500hPa..  L40: 36 vertical layers are defined in WRF and 9 vertical layers are defined in CHIMERE, the vertical layers are from 995 hPa (approximately 40m) up to over 500hPa. This is the low vertical grid resolution test with a high first layer height, a similar configuration of vertical resolution and first layer height used in the national air quality forecasting platform PREV'AIR. To assess the model abilities in simulating the vertical profiles of meteorological parameters and pollutant concentrations, two types of observation datasets including three meteorological observation stations operated by METEO FRANCE and SIRTA and four air qualityChapter IV 101/191

Figure4. 2

 2 Figure4.2 Maps of the mean 2m temperature (°C ); mean 10m wind speed (m/s) and mean surface relative humidity (%) over the period from 20 th November to 4 th December. Left column: results from L4; Middle column: results from L12; Right column: results from L40.

Figure4. 3

 3 Figure4.3 Maps of the mean NO 2 (μg/m 3 ); mean PM 2.5 (μg/m 3 ) and mean PM 10 (μg/m 3 ) mass concentrations over the period from 20 th November to 4 th December. Left column: results from L4; Middle column: results from L12; Right column: results from L40.

  5 and PM10concentrations for the corresponding first layer height to 1000m for the Paris region are displayed in Figure4.4. The vertical Kz profile shows similar shapes but different order of magnitudes from surface to the top. For all cases the Kz displays an inverse concentration-like distribution within the first 400m, low vertical grid resolution trend to produce stronger vertical mixing. The values of Kz at first layer are 1.48m 2 /s, 4.25m 2 /s and 15.37m 2 /s for L4, respectively, showing the model would generates a very poor mixing close to the surface. The pollutants profiles decrease with altitude. The vertical Kz in L12 is generally higher than L40, and the vertical distribution of pollutants is inversely proportional to Kz, which indicate that the main effect of increasing the vertical resolution is to increase the concentration of pollutants. Vertical profiles showing maximum concentrations of all pollutants at surface and decreasing faster with increasing altitude, this feature shows that adding an extra fine layer near surface may have an important impact on chemistry transport modelling.

Figure4. 5

 5 Figure4.5 Time series between the observed and modelled 2m temperature (°C); mean 10mwind speed (m/s) and planetary boundary layer height (PBLH)

  Figure4.6 Diurnal cycles for surface 2m temperature (°C); mean 10m wind speed (m/s) and planetary boundary layer height (PBLH) from 20 th November to 4 th December.4.3.3.2 Evaluation of main pollutants simulations

  steps. In order to calculate realistic meteorological input variables and pollutant concentrations, the system is configured with four nested-grid domains in Paris, Lyon and Bordeaux (as shown in Figure5. 1) include 106 × 91, 118 × 115, 115 × 103 and 85 × 79 grid points, with cell sizes of 45 (d01), 15 (d02), 5 (d03), and 1.67km (d04) horizontal resolution, respectively. The domain size and resolution are the same in the three areas. Parameters setting come from the best case in Chapter III for the WRF simulation. It means: the rapid radiative transfer model for GCMs longwave radiation scheme, the rapid radiative transfer model for GCMs shortwave radiation scheme, Monin-Obukhov surface layer scheme, Noah land surface scheme, multi-layer urban canopy scheme and Bougeault and Lacarrere (Boulac) boundary layer scheme.

Figure5. 1

 1 Figure5. 1 Four nested domains-from d01, the largest one, at a low resolution to d04, the finest one, at a high resolution.

Figure5. 3

 3 Figure5.3 Maps of the meteorological and air quality monitoring stations. Red dots represent urban background station; green dots represent suburban background station and blue dots represent rural background station.

Figure5. 6

 6 Figure5.6 Maps of the mean vertical diffusion coefficient (m 2 /s) at first layer; mean surface NO 2 mass concentration (µg/m 3 ) and mean surface O 3 mass concentration (µg/m 3 ) over the period from 20 th November to 4 th December. The first line: results from IKD scheme; the second line: results from NED scheme.

Figure5. 7 Figure5. 7

 77 Figure5.7 also shows the ability of the model to locate the pattern of the PE but also a clear underestimation of NO 2 concentrations during the PE for all stations. At most of the sites, the peak NO 2 concentration occurred during the daytime on December 1 st , the NO 2 exceeded 200µg/m 3 in the observations but the model does not effectively capture this moment with both schemes. The average mean bias in urban background stations for IKD and NED schemes during the PE are -39.88µg/m 3 and -24.16µg/m 3 respectively, showing a better ability of the NED simulation to reproduce primary pollutant concentrations

  Figure5.8 Diurnal cycles for surface Kz and surface concentrations of O 3 and NO 2 (µg/m 3 ) from 20 th November to 4 th December in urban background stations.

Figure5. 11

 11 Figure5.11 displays a comparison between the observed and modeled surface PM2.5 and PM10 at urban sites. The linear regression of PM2.5 and PM10 concentrations between observation and simulation can be found in Figure S5.3 and Figure S5.4 respectively. The direct comparison shows a fair reproduction of PM2.5 and PM10 concentrations during the RD but the models underestimate the concentrations on the PE. The fluctuations of PM2.5 and PM10 at Paris center station (FR04143) is obviously different with others which indicate that the emissions of PM at the center of the city is different with other sites, traffic emissions should be the major source of the FR04143 station. This also can be confirmed throughout the comparison between the fluctuations of NO 2 and PM in the corresponding station. Both schemes display similar behavior for the simulated period and display a reasonable representation of PM2.5 and PM10 observations. The simulation of PM10 is good on regular days but the model tends to slightly overestimate PM2.5 concentration at the same time. It indicates the slight overestimation of fine particles in the model. Two schemes underestimated PM concentration during the PE, the NED scheme gives a higher PM2.5 and PM10 concentrations than IKD scheme, which tends to be closer to the observations on the PE and the peaks of PM2.5 and PM10 are also enhanced from NED. As shows in Table5.5 and

Figure5. 13

 13 Figure5.13 Comparison of the time series of 2m temperature (°C ) and 10m wind speed (m/s) between the observations and different urban schemes at Bron, Exupery, Brindas and Merignac stations. The Bron, Exupery and Brindas stations are located in Lyon region and Merignac station is located in Bordeaux region. 5.3.4.2 NO2 Simulations over Lyon and Bordeaux regions 5.3.4.2.1 Focus on urban areas

  Figure5.14 Maps of the mean vertical diffusion coefficient (m 2 /s); over the period from 20 th November to 4 th December. The first line: results from Lyon domain; the second line: results from Bordeaux domain.

Figure5. 17

 17 Figure5.17 illustrates the diurnal cycles for surface Kz and surface concentrations of NO 2 during this period in urban sites for Lyon and Bordeaux domains. The diurnal variations of the surface Kz throughout both schemes shows similarities in Lyon but are very different in Bordeaux. In Bordeaux, the Kz from NED scheme remains stable only in the early morning and nighttime but Kz from IKD scheme had small changes throughout the day. Figure5.16(c) and Figure5.16(d) displays the diurnal cycles of surface NO 2 concentrations in Lyon and Bordeaux respectively. Both schemes capture the fluctuations well in both regions, displaying a bimodal distribution with the morning peak around 8:00-9:00 and the evening peak around 18:00-19:00 for observations and simulation. The IKD scheme tends to underestimate and the NED scheme tends to overestimate the surface NO 2 concentrations all day long in urban areas of those three cities (Paris, Lyon and Bordeaux).

Figure5. 17

 17 Figure5.17 Diurnal cycles for surface Kz and surface concentrations of NO 2 (µg/m 3 ) from 20 th November to 4 th December in urban background stations. Left side are the diurnal cycles of Kz and NO 2 in Lyon region and right side are the diurnal cycles of Kz and NO 2 in Bordeaux region.

  on the urban sites with slight differences in rural areas for both Lyon and Bordeaux domains.

Figure5. 19

 19 Figure5.19 Maps of the mean surface PM2.5 mass concentration (µg/m 3 ); over the period from 20 th November to 4 th December. The first line: results from Lyon domain; the second line: results from Bordeaux domain.

  5 and PM10) in Paris, Lyon and Bordeaux and Ozone (O 3 ) only in Paris. The performances of the NED and initial Kz diffusion (IKD) scheme were evaluated by ground observations both temporally and spatially.

  rural areas. Diurnal cycles indicate average vertical diffusion rate (Kz) based on IKD is approximately 2.5 times than the NED, the IKD scheme overall overestimates Kz in urban region and the NED scheme slightly underestimates Kz during nighttime. The model performance in Lyon and Bordeaux confirm the results obtained in Paris. The NED scheme improves the NO 2 simulation approximately by 24.51% and 9.52% in the urban areas of Lyon and Bordeaux respectively. Due to data limitations, model performances are only evaluated in the suburban areas of Lyon and rural areas of Bordeaux. Differently to Paris, the NED scheme better captures the fluctuations of NO 2 in the suburban of Lyon and Rural of

  Three boundary layer parametrization schemes coupled with urban canopy schemes (MYJ-Bulk, YSU-Bulk, MYJ-UCM, YSU-UCM, MYJ-BEP, and Boulac-BEP) and two land surface schemes (Noah and Noah-mp) has been tested in this part. This study aims at understanding theimpacts on air quality simulation with focus on a short-term pollution episode (PE) in the Paris region. CTM have been used with a first layer at 4m to better account for physical processes within the suburban layer in relation to ground emissions. The results indicate three canopy schemes could reproduce the 2m temperature (T2) effectively in the daytime; the Bulk and UCM model reproduced the 10m windspeed (W10) well, but the BEP model underestimates W10 for the whole episode. The UCM scheme overestimates the surface heat index in rural areas, leading to a slight negative bias for T2 in rural versus urban areas. NO 2 , PM2.5, and PM10 were selected to evaluate the effect of physics parameterizations on air quality simulation. Large differences were found in the simulation of pollutant concentrations, with an overall overestimation during the pollution episode. The overall boundary layer schemes display an underestimation of the PBL height practically during PE. The Boulac-BEP is the only scheme that could relative effectively reproduce the PBL height during PE. The Boulac-BEP scheme gives a significantly better performance on air quality simulation than the rest, indicating that both the canopy parameterization and boundary layer schemes have a significantly positive effect on air quality simulation in urban regions. The study on the land surface scheme shows it has slightly impact for both meteorological and air quality simulation. An advanced urban category could improve air quality prediction in urban regions.

Figure1. 3

 3 Figure1.3 Change in RF effects caused by atmospheric pollutants for the period 1750-2011, associated with individual confidence range.

Figure2. 3

 3 Figure2.3 Example of mass conservation.

Figure2. 4

 4 Figure2.4 Typical diurnal variation of the boundary layer structure. Sunrise (SR), Sunset (SS).

Figure2. 10 Figure3. 1

 101 Figure2.10 Diurnal cycles for surface concentrations (µg/m 3 ) of NO 2 , PM 2.5 and PM 10 in the year 2016

Figure3. 13

 13 Figure3.13 Vertical profiles of the CHIMERE Kz extracted at the FR04143 station. The profile is extracted at 06:00 and 22:00 UTC for both Regular Days (RD) and the Particulate Episode (PE).

Figure3. 14

 14 Figure3.14 Vertical profiles of the NO 2 and PM 2.5 mass concentration (µg m -3 ) at the FR04143 station. The profiles are extracted at 06:00 and 22:00 UTC for both Regular Days (RD) and the Particulate Episode (PE). Columns (a), (b), (c), and (d) represent the vertical mass NO 2 and PM 2.5 concentration at 28th November 06:00, 28th November 22:00, 30th November 06:00, and 30th November 22:00 UTC, respectively.

Figure3. 16

 16 Figure3.16 Time series between the observed and modelled T2: (a) and (b) represent Roissy and Sirta station, respectively; 1, 2, and 3 represent each PBL scheme coupled with each urban canopy scheme.

Figure3. 21

 21 Figure3.21 Comparison of the time series of 10m wind speed (m s -1 ) between the observations and different urban schemes at Montsouris, Roissy and Sirta stations.

Figure3. 22 Figure4. 2

 222 Figure3.22 Time series of NO 2 and PM 2.5 mass concentrations: No 1, 2, 3, and 4 represent station FR04002, FR04034, FR04143, and FR04156, respectively.

Figure4. 5

 5 Figure4.5 Time series between the observed and modelled 2m temperature (°C); mean 10m wind speed (m/s) and planetary boundary layer height (PBLH)

Figure5. 2

 2 Figure5.2 Maps of the meteorological and air quality monitoring stations. Red represent urban background station; green represent suburban background station and blue represent rural background station.

Figure5. 4

 4 Figure5.4 Time series between observed and modelled NO 2 mass concentrations (µg m -3 ) in urban and rural background stations.Figure5.5 Maps of the mean vertical diffusion coefficient (m s 2 ); mean surface NO 2 mass concentration (µg m -3 ) and surface mean O 3 mass concentration (µg m -3 ) over the period from 20 th November to 4 th December. The first line: results from IKD scheme; the second line: results from NED scheme.

Figure5. 6

 6 Figure5.6 Time series between observed and modelled NO 2 and O 3 mass concentrations (µg m -3 ) in urban background stations. Figure5.7 Diurnal cycles for surface kz and surface concentrations of O 3 and NO 2 (µg m -3 ) from 20 th November to 4 th December in urban background stations.

Figure5. 15

 15 Figure5.15 Time series between observed and modelled NO 2 mass concentrations (µg m -3 ) in urban background stations. FR20XXX represents the station in Lyon region and FR31XXX represents the station in Bordeaux region. Figure5.16 Diurnal cycles for surface kz and surface concentrations of NO 2 (µg m -3 ) from 20 th November to 4 th December in urban background stations. Left side are the diurnal cycles of kz and NO 2 in Lyon region and right side are the diurnal cycles of kz and NO 2 in Bordeaux region.

Figure S3. 5 .

 5 Figure S3.5. Time series between observed and modelled PM10: a, b and c represent each PBL schemes coupled with each urban canopy schemes respectively; 1, 2, 3, and 4 represent Station FR04002, FR04034, FR04143, FR04156, respectively.

Figure S3. 6 .

 6 Figure S3.6. Time series of bias between observed and modelled NO 2 : a, b and c represent each PBL schemes coupled with each urban canopy schemes respectively; 1, 2, 3, and 4 represent Station FR04002, FR04034, FR04143, FR04156, respectively.

Figure S3. 7 .

 7 Figure S3.7. Time series of bias between observed and modelled PM2.5: a, b and c represent each PBL schemes coupled with each urban canopy schemes respectively; 1, 2, 3, and 4 represent Station FR04002, FR04034, FR04143, FR04156, respectively.

Figure

  Figure S3.8. Time series of bias between observed and modelled PM10: a, b and c represent each PBL schemes coupled with each urban canopy schemes respectively; 1, 2, 3, and 4 represent Station FR04002, FR04034, FR04143, FR04156, respectively.

Figure. S3. 9 .

 9 Figure. S3.9. Liner fit between observed and modelled PM2.5 and PM10 .

  The simulation is performed from 1 st January to 31 st December 2016 at 7km resolution over France. The domain is discretized vertically with 20 vertical levels from 999.5hPa to 500hPa, with the lowest level at about 4m and 14 levels below 1000m. Meteorological input data were taken from the Integrated Forecasting System (IFS) model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Chemical boundary conditions were obtained from a lower (25km) resolution simulation performed on a European domain. The sector specific European Monitoring and Evaluation Program (EMEP) inventory (details can be

	0.01m 2 /s in the dry boundary layer and 1m 2 /s in the cloudy boundary layer in three schemes
	to avoid unreasonable low mixing (Menut et al., 2013a).
	The Kz from CHIMERE, CMAQ and
	FALL3D are defined as Kz1, Kz2 and Kz3 respectively. The Kz1 scheme is the initial vertical
	diffusion scheme of the CHIMERE model. The Kz2 and Kz3 schemes are the two most
	reasonable schemes selected on the basis of sensitivity experiments. A minimal Kz is setup to

found in https://www.emep.int/) has been used for anthropogenic emissions in the model, at a resolution of 0.1x 0.1.

  .1 Summary of the physics parameterizations used a

		LW	SW	SL LS	URB BL
	BULK	RRTMG RRTMG MO Noah	No	MYJ
	UCM	RRTMG RRTMG MO Noah	UCM MYJ
	BEP	RRTMG RRTMG MO Noah	BEP MYJ
	MYJ-BULK	RRTMG RRTMG MO Noah	No	MYJ
	YSU-BULK	RRTMG RRTMG MO Noah	No	YSU
	MYJ-UCM	RRTMG RRTMG MO Noah	UCM MYJ
	YSU-UCM	RRTMG RRTMG MO Noah	UCM YSU
	MYJ-BEP	RRTMG RRTMG MO Noah	BEP MYJ
	BOULAC-BEP RRTMG RRTMG MO Noah	BEP Boulac
	NOAH	RRTMG RRTMG MO Noah	BEP Boulac
	NOAH-MP	RRTMG RRTMG MO Noah-MP BEP Boulac

a LW= longwave radiation; SW= shortwave radiation; SL= surface layer; LS= land surface; URB= urban canopy; BL= boundary layer; RRTMG= rapid radiative transfer model for GCMs scheme; MO= Monin-Obukhov scheme; UCM= single-layer urban canopy scheme; BEP= multi-layer urban canopy scheme; MYJ= Mellor-Yamada-Janjic scheme; YSU= Yonsei University scheme; Boulac= Bougeault and Lacarrere scheme.

  For the studied period, 27 th November to 4 th December 2016, an 8-day spin-up period is performed for initialization issues. The vertical turbulent mixing in CHIMERE takes place only within the boundary layer, which is calculated by meteorological parameters such as surface temperature and the wind speed issued from the WRF simulation.

	The first CHIMERE layer is set to 4 m. The vertical dispersion in CHIMERE is mostly
	controlled by the PBL height and a turbulent flux modeled by a vertical eddy diffusion
	coefficient (K

is used for air quality modeling in the present study. CHIMERE is a state-of-the-art Eulerian offline CTM model; it requires pre-calculated boundary conditions, emission fluxes, and meteorological variables. z ). The horizontal diffusion is neglected in CHIMERE. The PBL height is

  The comparisons between observed and modelled surface main pollutants concentrations were performed. Although the model performances are uneven, all three configurations demonstrate the ability of chemistry transport modelling on RD. The impact of vertical grid resolution and first layer height is moderate. Taking the root mean square error as the Dans cette é tude, un sché ma de fermeture d'ordre 1.5 de la diffusion turbulente basé sur l'é nergie ciné tique de la turbulence, dé fini comme la « new eddy diffusion » (NED), est inté gré dans le modè le de transport chimique CHIMERE pour dé crire des processus de diffusion plus ré alistes prè s de la surface pour é tudier pollution urbaine. Une simulation de 15 jours incluant un é pisode de pollution hivernale a é té ré alisé e pour trois grandes villes franç aises avec une ré solution horizontale fine de 1,67km et une hauteur de premiè re couche de 12m. Le sché ma NED amé liore les simulations des concentrations de NO 2 pour chaque site urbain par rapport au sché ma de diffusion K initialement implante dans CHIMERE (IKD). En prenant l'erreur quadratique moyenne comme critè re d'é valuation, les amé liorations moyennes sont de 18,77%, 24,51% et 9,52% à Paris, Lyon et Bordeaux respectivement pour toute la pé riode. La simulation

	Chapter V
	Ré sumé
	evaluation criteria, L4 improves the model ability by 23.26% and 25.09% than L12 and L40
	respectively. However, on PE, improving vertical grid resolution and first layer height results
	clearly improves the air quality prediction, average improvements are 62.10% and 129.02%
	from L12 to L4 and from L40 to L4 respectively.
	This study highlights the model ability both in meteorological and chemistry transport
	modelling with low vertical resolution on RD, but such a model configuration displays
	discrepancies in representing the amplitudes of pollutants concentrations on PE and
	increasing the vertical resolution and refining first layer height leads to clear improvement
	during pollution episodes.
	respectively. The NED scheme improved NO 2 simulations in every urban site compare with the
	initial K diffusion scheme (IKD). Taking the root mean square error as the evaluation criteria,
	the average improvements are 18.77%, 24.51% and 9.52% in Paris, Lyon and Bordeaux
	respectively for the whole period. PM2.5 and PM10 simulation achieved same results with NO 2 ,
	a 13.47% and 19.08% improvement were found in the urban area of Paris respectively.
	However, the model performance for Particulate Matter in Lyon and Bordeaux probably
	indicate a huge underestimation of biomass burning emissions in wintertime. Sensitivity test
	shows that the model results are sensitive to changes of first layer height, over-refined first
	layer height will lead to unreasonable TKE simulations. Overall, preliminary outcomes of this
	study are encouraging. The simulation with more sophisticated and realistic eddy viscosities are
	better than IKD based on K-theory that is widely used in CTMs.
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des PM2.5 et PM10 a donné les mê mes ré sultats que celle du NO 2 , avec une amé lioration de 13,47% et 19,08% respectivement dans la zone urbaine de Paris. Cependant, les performances du modè le pour les particules à Lyon et Bordeaux indiquent probablement une é norme sous-estimation des é missions de la combustion de la biomasse en hiver. Le test de sensibilité montre que les ré sultats du modè le sont sensibles aux changements de la hauteur de la premiè re couche, une hauteur de premiè re couche trop raffiné e conduira à des simulations TKE dé raisonnables. Dans l'ensemble, les ré sultats pré liminaires sont encourageants dans cette é tude.

La repré sentation de sché mas de turbulence plus sophistiqué s et ré alistes semble amé liorer les simulations de chimie transport.

Keywords: turbulence; urban; air quality; vertical diffusion; winter episode

  Average modelled NO 2 mass concentrations (Avg); the correlation (R); mean bias (MB) and root mean square error (RMSE) between Observation and simulation of NO 2 infour urban background stations over Paris region. : The -improvement‖ is the relative change in % of the RMSE by using the initial Kz diffusion (IKD) coefficient and by using the new eddy diffusion (NED) coefficient.

	(µg/m 3 ) Table5.2 IKD FR04034 FR04002 Avg R FR04002 48.34 0.62 -5.11 MB Concentration FR04143 FR04034 31.12 0.74 -18.93 32.05 NO2-Observation NO2-IKD NO2-NED RMSE Avg 28.09 54.39 40.04	R 0.57 0.78	NED MB RMSE improvement a 0.94 30.29 -7.83% -10.01 25.89 19.22%
	Mass FR04143 47.98		0.67	-4.49	23.68	55.38	0.78	2.91	20.21	14.65%
	FR04156 FR04156 36.79 NO2 0.69	-12.87 30.68	40.81	0.72	-8.85	28.23	7.99%
	Table5.3 Average modelled O 3 mass concentrations (Avg); the correlation (R); mean bias
	21/11/2016 (MB) and root mean square error (RMSE) between Observation and simulation of O 3 in four 23/11/2016 25/11/2016 27/11/2016 29/11/2016 01/12/2016 03/12/2016 Date and Time
			urban background stations over Paris region.
	FR04017
			IKD				NED
	Avg		R	MB	RMSE	Avg	R	MB	RMSE improvement
	FR04017 17.45		0.73	5.30	13.82	10.33	0.67	-1.82	14.38	-4.05%
	FR04023		O3-Observation
	3 FR04023 35.81 ) (µg/m FR04098 35.81 Concentration FR04143 19.25	0.70 FR04098 0.63 0.73	14.05 16.23 5.83	O3-IKD O3-NED 22.25 23.64 13.35	31.16 28.62 11.59	0.64 0.54 0.66	9.40 9.04 -1.83	24.28 23.94 15.20	-9.12% -1.27% -13.86%
	Mass 5.3.2.2. Diurnal cycles of the surface Kz, NO2 and O3
	O3	FR04143
	00:00		12:00		00:00	12:00		00:00	12:00
	20/11/2016	22/11/2016	25/11/2016	27/11/2016	30/11/2016	02/12/2016
					Date and Time

a

  scheme slightly improved the NO 2 and O 3 prediction in suburban area but displays a poor performance in rural areas. Figure5.6 indicates that average Kz from NED scheme is around 0.3 m 2 /s in rural areas, this value is underestimated in NED scheme. However, there is only one background rural station of NO 2 in this region, therefore the result could be not robust enough. Average modelled NO 2 and O 3 mass concentrations (Avg); the correlation (R); mean bias (MB) and root mean square error (RMSE) between Observation and simulation of NO 2 and O 3 in suburban and rural background stations over Paris region.

	Table5.4 IKD			NED
				Avg	R	MB	RMSE	Avg	R	MB	RMSE improvement
	Suburban-NO 2 27.58 0.71 -10.40 24.95 32.76 0.69 -3.80	24.56	1.56%
	Rural-NO 2		23.62 0.64	-9.62	17.13 19.50 0.61 -4.44	19.07	-11.33%
	Suburban-O 3	37.77 0.61 17.65	21.33 21.80 0.45 1.68	20.97	1.69%
	Rural-O3		38.36 0.68 13.19	20.85 32.85 0.34 7.68	24.02	-15.02%
	Concentration (µg/m 3 )	100 40 0 160 80 120 200	a	Rural Suburban			NO2-NED NO2-Observation NO2-IKD
	Mass	80 60
	NO2	40 20
		0
			21/11/2016	23/11/2016	25/11/2016	27/11/2016	29/11/2016	01/12/2016	03/12/2016
							Date and Time
	(µg/m 3 )	60 100 80	b	Suburban
	O3 Mass Concentration	100 60 20 40 80 40 20 0		Rural
		0
			21/11/2016	23/11/2016	25/11/2016	27/11/2016	29/11/2016	01/12/2016	03/12/2016
							Date and Time
	Figure5.9 Time series between observed and modelled NO 2 and O 3 mass concentrations
		(µg/m 3 ) suburban background stations and rural background stations.

  the ratio is approximately 70% for the observation and around 90% for both simulations.Considering the overestimation of fine particles concentrations, this result shows that model have a strong underestimation of PM with particles exceeding 2.5µm. The model displays a reasonable behavior for both PM 2.5 and PM 10 on the PE, this indicates that the underestimation of pollutants on the PE is due to the underestimation of local urban pollutions sources, vehicle emission and other local sources should play important roles on this period.

					Chapter V
	)
	3
	(µg/m
	Concentration	150 100
	Mass	50
	PM2.5	0
		21/11/2016	25/11/2016	29/11/2016	03/12/2016
			Date and Time
	Figure5.11 Time series between observed and modelled PM2.5 and PM10 mass (µg/m 3 ) in
	urban; suburban and rural background stations. Urban background stations: FR04002;
	FR04034; FR04143 and FR04156. Suburban background stations: FR04024 and FR04181
		for PM 2.5 simulation and observation; FR04181 and FR04319 for PM 10 simulation and
	observation. Rural background stations: FR04048 for PM 2.5 simulation and observation;
				FR04158 PM 10 simulation and observation.
					141/191

but

4 Study in Lyon and Bordeaux regions

  Table5.5 Average modelled PM 2.5 mass concentrations (Avg); the correlation (R); mean bias (MB) and root mean square error (RMSE) between Observation and simulation of PM 2.5 in urban, suburban and rural background stations over Paris regions.

	IKD MB FR04002 24.17 0.63 -1.35 Avg R FR04034 22.18 0.62 -3.34 FR04143 27.93 0.72 2.41 FR04156 23.53 0.66 -2.77 Suburban FR04024 21.82 0.64 -0.51 Urban FR04181 20.98 0.75 1.10 Rural FR04048 21.61 0.70 2.82 Table5.6 Average modelled PM10 mass concentrations (Avg); the correlation (R); mean bias NED RMSE Avg R MB RMSE improvement 18.23 27.69 0.74 2.17 16.22 11.03% 20.27 27.28 0.81 1.76 15.59 23.09% 16.26 32.66 0.86 7.13 14.38 11.56% 22.74 27.42 0.74 1.12 20.88 8.18% 14.72 25.70 0.73 3.38 13.46 8.56% 12.27 23.66 0.77 3.78 12.02 2.04% 10.96 24.71 0.84 5.92 11.51 -4.78% (MB) and root mean square error (RMSE) between Observation and simulation of PM10 in urban, suburban and rural background stations over Paris regions. IKD NED Avg R MB RMSE Avg R MB RMS E improvement Urban FR04002 26.56 0.66 -10.98 25.76 29.7 2 0.76 -7.82 22.08 14.29% FR04034 24.15 0.62 -13.42 30.38 29.26 0.80 -8.31 23.44 22.84% FR04143 30.34 0.70 -4.50 21.38 34.91 0.86 0.07 15.19 28.95% FR04156 25.70 0.70 -12.16 33.55 29.38 0.77 -8.48 30.11 10.25% Suburban FR04181 22.55 0.76 -4.76 14.35 24.86 0.78 -2.45 13.18 8.88% FR04319 25.34 0.57 -15.92 39.58 29.39 0.66 -11.87 36.60 7.53% Rural FR04158 24.91 0.81 -3.20 24.57 28.60 0.84 0.37 22.44 -8.07% Table5.7 Proportion between PM2.5 and PM10 in urban, suburban and rural background stations for the whole period (WP) and pollution episode (PE) over Paris regions. WP PE Obs IKD NED Obs IKD NED Urban 70.73% 91.61% 93.31% 71.27% 88.35% 90.49% Suburban 61.73% 89.35% 90.96% 59.22% 85.04% 86.22% Rural 78.24% 86.75% 86.40% 86.19% 89.56% 92.29% 5.3.Chapter V
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  The flow characteristics and dispersion mechanisms of pollutants are important for air quality simulation and forecast. The first part of the study illustrates the difficulties to improve the ability of the model through the simplest first order local closure schemes.

	L40: 49 vertical levels are defined in WRF and 20 vertical layers are defined in CHIMERE
	from 995hPa to 500hPa, the first layer is about 40m. Results show the impact is relatively
	small in meteorological simulations. The relative bias (RB) of L12 and L40 compare with L4
	(L12-L4 and L40-L4) are 2.17% and 22.95% for 2m temperature (T2), 4.01% and -12.69%
	for 10m wind speed (W10), -0.12% and -2.72% for surface relative humidity (RH),
	respectively. On regular days, all three configurations demonstrate the ability of chemistry
	transport modelling. L4 improved model ability of 23.26% and 25.09% compare with L12
	and L40 respectively. During pollution episodes, refined vertical grid resolution and first layer
	height produces a significant improvement in chemistry transport modeling, improvements
	are 62.10% and 129.02% in L4 compare with L12 and L40.
	Fifteen days sensitivity tests with multiple vertical grid resolution and first layer height
	defined as (i) L4: 53 vertical layers are defined in WRF and 20 vertical layers are defined in
	CHIMERE. The first layer of the two model is at 999.5hPa (approximately 4m) up to over
	500hPa; (ii) L12: 51 vertical levels are defined in WRF and 20 levels are defined in
	CHIMERE, starting with the first vertical height at about 12 meters above the ground; (iii)

  The NED scheme is not performing as well as IKD in rural areas. Diurnal cycles between the vertical diffusion coefficient and pollutants indicate the IKD scheme overall overestimate diffusion rate in urban region and NED scheme slightly underestimated it during the nighttime. The model performances in Lyon and Bordeaux are rather good over Paris, NED scheme improved the NO 2 simulation approximately 24.51% and 9.52% in the urban areas of Lyon and Bordeaux respectively. However, PM simulation results over those two latter areas could indicate a huge underestimation of biomass burning emissions in wintertime., Both schemes can capture the evolution of PM2.5 and PM10, the average concentrations are strongly underestimated particularly in Bordeaux and on the PE of Lyon. The NED scheme gradually improves the air quality simulation as it approaches the city center. Sensitivity tests to grid resolution showed that the model results are sensitive to the vertical resolution and do not tend to improve with increasing resolution, there configuration of parameters are weak under the displacement height, the aspect ratio is curial on eddy simulation. Overall, surface turbulent motions were better preserved by a 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme than those with the simplest ones.Since the 1970s, the CTMs have developed from the initial simple box model to today's complex third generation CTM models. The configurations of CTM should be combined with the characteristics of the research problem, the time and space scale of the simulation, data availability, technical complexity, software and hardware platform requirements, etc. Not all simulations have to choose the highest model configuration. Advanced configurations generally have very strict requirements on basic data, hardware platform, professional threshold, etc., and the cost will be several times compared with the basic configurations. example, the ratio of PM2.5 to PM10 is above 90% in every studies of this research, however, the value is approximately 70% in the real atmospheric. In coastal cities, the modeled particulate matter concentrations are far from the observation. The lack of emissions directly leads to the poor performance of models. The completeness of emission inventories particularly on residential emissions remains challenging for the air quality community.Urban-induced atmospheric circulations, though typically small scale, can have a significant impact on mesoscale dynamics. Many studies indicate that refining urban landuse categories helps improving the model's ability to simulate the processes in the urban canopy. However, it could present notable challenges for mesoscale models due to the tremendous horizontal and vertical variability of the urban landscape and the variety of surface forcing. A lower first model layer could better reproduce the transport and diffusion of pollutants in a real urban environment. However, urban canopy is an open canopy, a refined urban category could increase the displacement height in the cities leads a very weak configuration of parameters at low level, it requires more studies. Future works should also focus on the comparison of pollutant concentrations over the vertical within the urban canopy to better evaluate the performance of models.

	Therefore, choosing an appropriate model configuration is the key to achieving the goals with
	the lowest cost. From the perspective of air quality model application practice, it is often
	difficult for users to accurately grasp the applicable conditions of the model or simplify
	complex issues.
	With the rapid development of high-performance computers, high resolution numerical
	simulation has achieved great success. However, most high-resolution simulations are still at
	the research stage, the time span is usually from days to a month. Although computer
	resources are not extravagant, long time series high-resolution simulation is still difficult to
	achieve. Thus, a technique that could pass the costly high simulation is important. The

uncertainty of the CTM mainly comes from five parts: chemical mechanism; chemical reaction rate; boundary layer; emission inventory and meteorological data. Take the model performance of particulate matter as an

  , 0.0915945247, 0.0000, Figure S5.2. Time series between observed and modelled kz, NO 2 and O 3 mass concentrations in urban background station over Paris region. Table S5.1. Average modelled NO2 and O 3 mass concentrations (Avg); the correlation (R); mean bias (MB) and root mean square error (RMSE) between Observation and simulation of NO 2 and O 3 in urban background stations over Paris region : The -improvement‖ is the relative change in % of the RMSE by using the initial kz diffusion coefficient and by using the new eddy diffusion coefficient.

							Annex B
		gfdda_end_h			= 10000, 10000, 255, 255,
		gfdda_interval_m			= 360, 360, 360, 360,
		io_form_gfdda			= 2,
		fgdt				= 1, 1, 1, 1,
		if_no_pbl_nudging_uv			= 0, 0, 0, 0,
		if_no_pbl_nudging_t			= 0, 0, 0, 0,
		if_no_pbl_nudging_q if_zfac_uv (µg/m 3 )			= 1, 1, 1, 1, = 0, 0, 0, 0,
		k_zfac_uv if_zfac_t NO2	0 80 40			= 12, 12, 12, 12, = 1, 1, 1, 1,
		k_zfac_t				= 12, 12, 12, 12,
		if_zfac_q				= 1, 1, 1, 1,
		k_zfac_q				= 12, 12, 12, 12,
		if_zfac_ph				= 1, 1, 1, 1,
		k_zfac_ph				= 12, 12, 12, 12,
		guv	21/11/2016	23/11/2016	= 0.0003, 0.0003, 0.0003, 0.0003, 25/11/2016 27/11/2016 29/11/2016 01/12/2016	03/12/2016
		gt				= 0.0003, 0.0003, 0.0003, 0.0003, Date and Time
		gq				= 0.0003, 0.0003, 0.0003, 0.0003,
		gph				= 0.0003, 0.0003, 0.0003, 0.0003,
		xwavenum			= 5, 5, 5, 5,
		ywavenum			= 5, 5, 5, 5,
		if_ramping			= 0,
	/	dtramp_min			= 60.0,
	&physics /
	mp_physics &dynamics				= 6,		6,	6,	6,
	ra_lw_physics w_damping			= 4, = 1,	4,	4,	4,
	ra_sw_physics rk_ord			= 4, = 3,	4,	4,	4,
	radt diff_opt			IKD	= 30, = 1,	30, 1,	30, 1,	30,	2,	NED
	bldt km_opt	Avg	R	= 0, = 4, MB RMSE 0, 4,	0, Avg 4,	0,	R	2,	MB	RMSE improvement a
	cu_physics cudt isfflx diff_6th_opt diff_6th_factor Urban-NO2 41.06 0,74 -10.40 24.90 47.99 0.80 -3.80 = 1, 1, 1, 0, = 5, 5, 5, 5, = 1, = 2, 2, 2, 2, = 0.12, 0.12, 0.12, 0.12, base_temp = 290, Urban-O3 27.08 0.74 11.29 16.65 20.41 0.64 4.63	20.40 17.47	18.77% -4.80%
	ifsnow damp_opt				= 1, = 1,
	icloud zdamp				= 1, = 5000., 5000., 5000., 5000.,
	surface_input_source dampcoef			= 3, = 0.01,			0.01,	0.01,	0.01,
	num_soil_layers khdif			= 4, = 0,	0,	0,	0,
	num_land_cat kvdif			= 21, = 0,	0,	0,	0,
	maxiens smdiv = 0.1, 0.1, 0.1,			= 1,
	maxens emdiv = 0.01, 0.01, 0.01,			= 3,
	maxens2 epssm = 0.1, 0.1, 0.1,			= 3,
	maxens3 non_hydrostatic			= 16, = .true., .true., .true., .true.,
	ensdim moist_adv_opt			= 144, = 1,			1,		1,	2,
	sf_sfclay_physics scalar_adv_opt			= 2, = 1,	2,	1,	2,	1,	2,	2,
	sf_surface_physics tke_adv_opt			= 2, = 1,	2,	1,	2,	1,	2,	2,
	sf_urban_physics time_step_sound			= ( 0 for Bulk; 1 for UCM; 2 for BEP) = 4, 4, 4, 4,
	bl_pbl_physics h_mom_adv_order			= ( 1 for YSU; 2 for MYJ; 8 for Boulac) = 5, 5, 5, 5,
	num_urban_layers v_mom_adv_order			= 6400 = 3,			3,	3, 3,
	/	h_sca_adv_order			= 5,			5,		5, 5,
	&fdda v_sca_adv_order			= 3,			3,		3, 3,
		grid_fdda				= 1, 1, 0, 0,
	/	gfdda_inname			= "wrffdda_d<domain>",
							187/191

a

Supplementary material for the publication is provided in Annex A

Results at the three meteorological stations are compared with the observations to evaluate the canopy scheme's performance. Figure3.8 presents the time series of hourly-averaged T2

during the whole period. The observed T2 varies between -1.4°C and 9.3 °C , -3.9°C and 7.8 °C , and -2.1°C to 8.4°C , with an average of 4.0°C , 2.6°C, and 4.9°C at Montsouris, Roissy, and Sirta stations, respectively. Compared with the observations, all three schemes tended to underestimate the T2 during the period 16:00 pm-10:00 am UTC on the 27 th and 28 th for all stations, while higher positive values were observed around midnight of the 1st

December for Montsouris and Roissy stations, especially at Roissy, with a positive bias from 1.8°C to 4.9°C during the PE. The BEP scheme simulates relatively higher T2 at PE for all the stations because the BEP scheme simulates a stronger UHI than the other two schemes.

All the schemes have the best performance at the Sirta site. Compared with the observations from three stations, the differences in the modelled T2 are minimal between three stations during the PE. However, there are significant differences in the observed T2 between the stations. For example, the minimum observed T2 are -1.4°C , -3.9°C , and -0.3°C during the midnight of 1 st December at Montsouris, Roissy, and Sirta stations, respectively. A possible reason could be the use of a single urban category in our WRF simulations, but in the real environment Montsouris and Sirta stations are closer to commercial and residential areas and Roissy is a synoptic station close to the airport, which could be assimilated as a rural environment with a weaker UHI effect, especially during nighttime.

Air Quality Simulations

The impacts of the PBL schemes on air quality are analyzed in this section. The time series of the model predictions of the NO 2 , PM2.5, PM10 concentrations for all the schemes are compared with the observations (the NO 2 can be found in Figure3.18; the PM2.5 and PM10 can be found in Figures S4 andS5). In general, all the schemes have a better performance for the RD than the PE period, and almost all the schemes strongly overestimate pollutant concentrations at the beginning of the PE, except the Boulac-BEP scheme. The bias between the modelled and observed pollutant concentrations can be found in Figures S6, Figure S7, and Figure S8. All the schemes give a worse performance for NO 2 and PM2.5 prediction than for PM10. The PBL height reaches the minimum value at 18:00 UTC on 29th November, with the YSU-Bulk scheme corresponding to an underestimation of 500 m; such a difference creates a large bias in air pollutant concentrations. The same phenomenon occurs for the MYJ-Bulk, MYJ-UCM, YSU-UCM, and MYJ-BEP schemes at 00:00 UTC on 30 th November. However, the overestimation of pollutant concentrations does not occur at this time but at 06:00 UTC on 1st December, because this episode is largely influenced by local sources (road traffic and residential heating). On 1st December 2016 at 06:00 UTC (a weekday), the morning rush hour leads to a lag effect in the bias, explaining why all the schemes have their worst performance for the NO 2 and PM2.5 predictions. The averaged statistics for the four air quality monitoring stations are summarized in Table3.4. Figure3.19 gives the scatterplot between the observed and modelled NO 2 ; the scatterplot for the PM2.5 and PM10 can be found in Figure S9. Overall, the MYJ scheme overestimates the mass concentrations of all pollutants, whatever the selected urban canopy model. The Boulac-BEP scheme has a significantly better performance than other schemes for all the evaluated variables. The YSU-Bulk scheme has the lowest mean bias (-1.3 µg/m 3 ) in PM10 prediction, but its mean linear correlation coefficients of NO 2 , PM2.5, and PM10 were 0.52, 0.40, and 0.53, respectively. The mean biases for the NO 2 and PM2.5 prediction were 17.63µg/m 3 and 46.0µg/m 3 , respectively. The low correlation for all the analyzed variables, positive biases for NO 2 and PM2.5 prediction, and negative bias for PM10 prediction indicate that this scheme under and over-estimates the concentrations simultaneously, which means that the YSU-Bulk scheme cannot be performed effectively for the prediction of short-term PE in urban regions.

The Boulac-BEP scheme was better for all the analyzed variables, both in terms of bias and correlation statistics; its mean biases for NO 2 , PM2.5, and PM10 prediction were -5.13µg/m 3 , The time evolution of hourly-averaged NO 2 , PM2.5 and PM10 between observation and three cases involved in the inter-comparison is provided in Figure4.7. The results show that all three cases captured fairly well the day-to-day variability of the surface pollutants concentrations on RD but generally underestimated the intensity during the PE days. A relatively small differences between cases can be found from 20 th to 29 th November. While refining the first layer height has not a very sensitive impact on meteorology, it has a significant beneficial impact on air quality simulation. As shown in Table4.4, L4 case gives a clear best performance for of pollutants. The agreement with observations is generally better than for the other two cases, the correlations are 0.87, 0.90 and 0.91 in the cases of NO 2 , PM2.5 and PM10 respectively. Compared to the observations, the peak of pollutants corresponding to the PE is well modeled in L4 simulation, although it still underestimates surface concentrations on PE. The changes of pollutants concentrations are small between RD and PE in L40, which indicates low vertical resolution can hardly predict the PE effectively.

The model comparison in Figure4.8 presents the differences between cases. Positive biases are found in L12-L4 and negative biases are found in L40-L4 from 20 th to 28 th November. On reason is the relatively low PBLH of L12 and relatively high PBLH of L40 compare with L4 over this period. Since 29 th November, the surface pollutants concentrations obtained from L4 are mostly higher than other two cases. While the average PM 2.5 and PM 10 surface concentrations are 25.64μg/m 3 and 36.25μg/m 3 in the observations, in the simulations, the values are 27.42μg/m 3 and 29.67μg/m 3 from L4, 24.45μg/m 3 and 26.69μg/m 3 from L12, 14.82μg/m 3 and 16.24μg/m 3 from L40 respectively. This result is consistent with our one-year simulation over French region in Chapter II, the ratio of PM2.5/PM 10 generally over 90% in the model. The reason could originate from the overestimation of fine mode emissions, as well as the lack of natural coarse emissions (Im et al., 2010;Vautard et al., 2009). In general, emission inventory needs improvement.

Figure5.2 General principle of the NED simulation

The Reynolds decomposition prognostic equation for TKE e under the assumptions of horizontal homogeneity can be written as (5.2):

Where u, v, w are the wind components, is the mean virtual potential temperature, p is the pressure, is the density, g is the gravitational acceleration and ε is the viscous dissipation of TKE [START_REF] Stull | Turbulence Kinetic Energy, Stability and Scaling[END_REF].

Chapter V

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The TKE order-1.5 is also constructed upon the gradient transport theory as (5.3):

(5.

3)

The left side represents the Reynolds-average operator, the vertical turbulent flux is parameterized as the product of local gradient of analytical variable and eddy diffusivity K.

In Boulac scheme, vertical eddy diffusivity defined as (5.4):

Where C k is a constant set up to 0.7, l is the vertical length scale [START_REF] Deardorff | Stratocumulus-capped mixed layers derived from a three-dimensional model[END_REF], and e is the TKE defined as (5.5):

( ' ' ' )

(5.5)

In the Boulac scheme, it is assumed that at individual level in the atmosphere, the length scale l may be related to the distance that the parcel originating from the corresponding level and having the initial kinetic energy equal to the average TKE of the layer which can move up and down before being prevented by the buoyancy effect. The and is defined as (5.6):

Thus, the length scale l must be related to some value between and . In Boulac scheme, the l is written as (5.7):

The advantage of this scheme is to allow for remote effects of stable zones to define the turbulence mixing length scale (Bougeault and Lacarrere, 1989). This scheme solves a prognostic TKE equation which includes TKE advection and parameterizes sources and sinks from shear production, buoyancy, mixing and dissipation (Simon et al., 2019). The model physical parameterizations remain the same as Boulac-BEP scheme detailed in Chapter III, (see Table3.1 of Chapter III).

List of Tables

Table2.1

The kz (m 2 /s) values within 10 levels Table2.2 Seasonal average mass concentration of NO 2 , PM 2.5 and PM 10 (µg/m 3 ) between observation and three schemes.

Table2.3 Statistics of surface variables: mean linear correlation coefficient (R); mean bias (MB) and root mean square error (RMSE) between the simulations and observations of NO 2 , PM 2.5 and PM 10 in Urban and Rural areas.

Table3.1 Summary of the physics parameterizations used.

Table3.

LIST OF TABLES AND FIGURES

Table S3.1. The urban parameters for the urban canopy model.