domain: phys spi info math

text:
Tiphaine Sabatier
email: tiphaine.sabatier@gmail.com

Claire Sarrat

Sébastien Aubry

Thomas Chaboud

Quantification of the airport-related pollution under wintertime anticyclonic conditions from idealized Large-Eddy Simulations

Keywords: Airport Pollution, Large-Eddy Simulation, CAEPport, Nitrogen Dioxide, Photo-chemistry

Introduction

Air quality in urban and suburban areas is a major concern in many countries around the world. Although urban pollution is widely documented [START_REF] Britter | Flow and dispersion in urban areas[END_REF][START_REF] Vautard | A synthesis of the air pollution over the paris region (esquif) field campaign[END_REF][START_REF] Vardoulakis | Modelling air quality in street canyons: a review[END_REF][START_REF] Cros | The escompte program: an overview[END_REF][START_REF] Lateb | On the use of numerical modelling for near-field pollutant dispersion in urban environmentsa review[END_REF], air quality in airport environments has received less attention while aviation grows at an annual rate of around 5 % (CAEP, 2013;[START_REF] Masiol | Aircraft engine exhaust emissions and other airportrelated contributions to ambient air pollution: A review[END_REF]. The contribution of air traffic to air pollution is therefore steadily increasing and causes adverse health effects for passengers, airport workers and nearby residents [START_REF] Rogers | The impacts of aviation on the atmosphere[END_REF][START_REF] Schürmann | The impact of NOx, CO and VOC emissions on the air quality of Zurich airport[END_REF][START_REF] Ellermann | Assessment of the air quality at the apron of copenhagen airport kastrup in relation to the working environment[END_REF][START_REF] Yim | Air quality and public health impacts of uk airports. part ii: Impacts and policy assessment[END_REF][START_REF] Hudda | International airport impacts to air quality: size and related properties of large increases in ultrafine particle number concentrations[END_REF].

The airport-related emissions sources are numerous and their relative contributions are still discussed in the literature [START_REF] Mazaheri | An inventory of particle and gaseous emissions from large aircraft thrust engine operations at an airport[END_REF][START_REF] Masiol | Aircraft engine exhaust emissions and other airportrelated contributions to ambient air pollution: A review[END_REF][START_REF] Yim | Global, regional and local health impacts of civil aviation emissions[END_REF]. Among them, a significant part originates from the engine fuel burning which produces carbon dioxide, water vapor, nitrogen oxides, hydrocarbons, sulphur oxides, particulate matter, and other trace compounds [START_REF] Yang | Characterization of aircraft emissions and air quality impacts of an international airport[END_REF]. These compounds are mainly released by (i) the Landing and Take-off (LTO) cycles which include the idle, approach, take-off and climbing phases according to the International Civil Aviation Organization (ICAO) and (ii) the ground service operations through the use of Auxiliary Power Units (APU), ground power units or ground service equipment [START_REF] Mazaheri | An inventory of particle and gaseous emissions from large aircraft thrust engine operations at an airport[END_REF][START_REF] Padhra | Emissions from auxiliary power units and ground power units during intraday aircraft turnarounds at european airports[END_REF]. Despite the harmful health effects of particulate matter [START_REF] Hudda | International airport impacts to air quality: size and related properties of large increases in ultrafine particle number concentrations[END_REF][START_REF] Habre | Short-term effects of airport-associated ultrafine particle exposure on lung function and inflammation in adults with asthma[END_REF], this work only deals with gaseous species. More precisely, the nitrogen dioxide (NO 2 ) is the primary focus of this study as it causes respiratory and cardiovascular diseases [START_REF] Bernstein | Health effects of air pollution[END_REF][START_REF] Who | Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide[END_REF][START_REF] Samoli | Short-term effects of nitrogen dioxide on mortality: an analysis within the aphea project[END_REF][START_REF] Zhao | Ambient air pollution and daily outpatient visits for cardiac arrhythmia in shanghai, china[END_REF][START_REF] Santurtún | The influence of nitrogen dioxide on arrhythmias in spain and its relationship with atmospheric circulation[END_REF]. The NO 2 air quality standards are fixed by the European Union Directive 2008/50/EC which provides an hourly limit of 200 µg.m -3 . Among the few available studies, a large variability in the measured concentrations is noticeable depending on the documented LTO phase. For instance, [START_REF] Schäfer | Aircraft emission measurements by remote sensing methodologies at airports[END_REF] presented NO 2 measurements obtained from a differential optical absorption spectrometer installed behind aircraft during the idle phase. Their 15-second measurements highlighted 2 a high variance in emissions depending on engine type with NO 2 concentrations varying between 20 and 340 µg.m -3 . In comparison, measurements were also performed 25 m behind aircraft during the AIRPUR field-experiment in order to characterize the pollutant levels under several engine power settings [START_REF] Lelièvre | Air quality and engine emission at paris cdg airport during airpur field campaigns[END_REF]. By switching the engine from low power thrust (idle) to maximum power thrust (take-off), the authors reported a rise in NO 2 concentrations from 5 to 1600 µg.m -3 in less than 15 minutes. At London Heathrow, autumnal 10-second NOx (NOx = NO+NO 2 ) measurements were performed from a dualchamber chemiluminescent analyzer located 180 m north of a runway [START_REF] Carslaw | Near-field commercial aircraft contribution to nitrogen oxides by engine, aircraft type, and airline by individual plume sampling[END_REF].

The analysis of a daily time series revealed a large variability with several peaks exceeding 1000 µg.m -3 . The same concentration range was measured within 550 m of a runway at JFK New-York Airport under summertime conditions [START_REF] Herndon | NO and NO2 emission ratios measured from in-use commercial aircraft during taxi and takeoff[END_REF]. At the Venice international airport, [START_REF] Valotto | Characterization of hourly nox atmospheric concentrations near the venice international airport with additive semi-parametric statistical models[END_REF] analyzed daily averaged NOx concentrations measured over one year with a chemiluminescent sensor located 500 m away from the runway. The authors highlighted a seasonal cycle with highest concentrations recorded during the cold season. At that time, the daily averaged NOx concentrations were around 150 µg.m -3 while several maxima above 300 µg.m -3 were reached. This concentrations variability results from (i) the difference in meteorological situations, (ii) the difference in measurement techniques and sensor locations and (iii) the difficulty to get representative concentrations from local measurements. Besides, the quantification of the airport contribution from measurements only is delicate because of the proximity between airports, roads and urban centers, which all participate to local pollution [START_REF] Carslaw | Detecting and quantifying the contribution made by aircraft emissions to ambient concentrations of nitrogen oxides in the vicinity of a large international airport[END_REF]. Finally, these measurements-based studies highlight our limited knowledge of the air quality at the platform scale and underline the need for numerical studies to provide a complete overview of the spatio-temporal plume development.

Several types of tools are available to simulate airport pollutants dispersion depending on the the spatio-temporal scales involved. For example, Gaussian or Lagrangian models as ADMS [START_REF] Peace | Identifying the contribution of different airport related sources to local urban air quality[END_REF], EDMS [START_REF] Unal | Airport related emissions and impacts on air quality: Application to the atlanta international airport[END_REF] or LASPORT [START_REF] Janicke | Lasport -a model system for airport-related source systems based on a lagrangian particle model[END_REF] have proved able to represent long term and annual trends or daily means of pollutants exposure. The simple parametrization of the atmospheric boundary layer and the imprecise emission inventory (LTO cycle estimations) do not allow a fine spatio-temporal concentration accuracy but only a mean exposure. Moreover, the Gaussian or Lagrangian approximations are not possible under light wind conditions, which is a strong limitation of these models.

Meteorological models have been used to simulate the airport impact on regional air quality using using Chemistry Transport Models like CHIMERE [START_REF] Pison | Quantification of the impact of aircraft traffic emissions on tropospheric ozone over paris area[END_REF] or the EPA Community Multi-scale Air Quality (CMAQ) [START_REF] Arunachalam | Effect of chemistrytransport model scale and resolution on population exposure to pm2.5 from aircraft emissions during landing and takeoff[END_REF][START_REF] Rissman | A plume-in-grid approach to characterize air quality impacts of aircraft emissions at the hartsfield-jackson atlanta international airport[END_REF][START_REF] Woody | Estimates of nontraditional secondary organic aerosols from aircraft svoc and ivoc emissions using cmaq[END_REF]. These studies highlighted that airports represent a second order source of pollution at regional scale comparing to road traffic, residential or industrial sources. However, these simulations are not able to provide information about the exposure of the airport itself or its close vicinity.

As the aim of the present study is to characterize the pollution plume features (intensity, spatial expansion and homogeneity) reached over an airport during a daily cycle, a high spatio-temporal resolution model is chosen. A focus is made on an academic wintertime anticyclonic situation which is often associated with poor air quality because of stagnant weather conditions and weak dispersion [START_REF] Pope | The influence of synoptic weather regimes on uk air quality: analysis of satellite column no2[END_REF][START_REF] Grundstrom | Influence of atmospheric circulation patterns on urban air quality during the winter[END_REF][START_REF] Santurtún | The influence of nitrogen dioxide on arrhythmias in spain and its relationship with atmospheric circulation[END_REF][START_REF] Yang | Characterization of aircraft emissions and air quality impacts of an international airport[END_REF]. A 14-hour Large Eddy Simulation (LES) is run with a non-hydrostatic atmospheric model (Meso-NH) which includes a semi-empirical photochemical module. Meso-NH is coupled with a high-resolution emission inventory providing realistic traffic conditions between 0600 and 2000 UTC. The simulation is initialized in an idealized way with a clean atmosphere (no large scale or previous day residual pollution). This configuration allows the isolation and quantification of the airport-related pollution by ignoring road traffic and urban-related emissions. In addition, a sensitivity analysis is conducted in order to assess the effects of an airport traffic doubling on the plume features.

The numerical configuration and the emission inventory are presented in Section 2. Section 3 is dedicated to the study of the reference simulation while the sensitivity analysis is the purpose of Section 4. The results are discussed in Section 5 and the main conclusions are drawn in Section 6.

Methodology

Numerical simulations are run by coupling a mesoscale atmospheric model (Meso-NH) with a high-resolution emission inventory. Both components are described in the current section.

The Meso-NH model

In this study, the non-hydrostatic atmospheric research model Meso-NH [START_REF] Lac | Overview of the meso-nh model version 5.4 and its applications[END_REF] is used. This model can be run from synoptic to turbulent scales and has already shown its performance in modeling air quality and dispersion [START_REF] Tulet | Description of the mesoscale nonhydrostatic chemistry model and application to a transboundary pollution episode between northern france and southern england[END_REF][START_REF] Sarrat | Impact of urban heat island on regional atmospheric pollution[END_REF][START_REF] Lac | CO2 dispersion modelling over Paris region within the CO 2-MEGAPARIS project[END_REF] along with atmospheric dynamics over an airport [START_REF] Bergot | Effect of small-scale surface heterogeneities and buildings on radiation fog: Large-eddy simulation study at paris-charles de gaulle airport[END_REF][START_REF] Sarrat | Modelling airport pollutants dispersion at high resolution[END_REF].

Numerical configuration

Regarding the numerical configuration, a Forward-In-Time temporal scheme is used. A 1 second time step is applied in order to respect the Courant-Friedrichs-Lewy criterion.

Advection is resolved by the Piecewise Parabolic Method (PPM - [START_REF] Colella | The piecewise parabolic method (ppm) for gas-dynamical simulations[END_REF]) for meteorological and chemical variables and by the fourth-order centered scheme for momentum. Cyclic lateral boundary conditions are applied for meteorological variables while chemical species are treated with a zero-gradient condition. The radiation scheme relies on the [START_REF] Fouquart | Computations of solar heating of the earth's atmospherea new parameterization[END_REF] algorithm for short-wave radiation and on the Rapid Radiative Transfer Model (RRTM - [START_REF] Mlawer | Radiative transfer for inhomogeneous atmospheres: Rrtm, a validated correlated-k model for the longwave[END_REF]) for long-wave radiation.

Concerning turbulence, the 3D scheme described by [START_REF] Cuxart | A turbulence scheme allowing for mesoscale and large-eddy simulations[END_REF] is used with a prognostic equation for sub-grid Turbulent Kinetic energy (TKE) (1.5 closure) and the diagnostic mixing length of [START_REF] Deardorff | Three-dimensional numerical study of turbulence in an entraining mixed layer[END_REF]. Meso-NH is online coupled at each time step with the surface model SURFEX [START_REF] Masson | The surfex v7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes[END_REF] to represent the surface-atmosphere interactions. Momentum, heat and water fluxes are determined by the ISBA scheme [START_REF] Noilhan | A simple parameterization of land surface processes for meteorological models[END_REF] for natural covers and by the TEB scheme [START_REF] Masson | A physically-based scheme for the urban energy budget in atmospheric models[END_REF] for urbanized surfaces. With this approach, the characteristics of each tile (texture, roughness length...) are considered allowing a proper representation of the surface-atmosphere exchanges at fine scale. The interactions with buildings are represented through a drag force approach as proposed by [START_REF] Aumond | Including the drag effects of canopies: real case large-eddy simulation studies[END_REF] and [START_REF] Bergot | Effect of small-scale surface heterogeneities and buildings on radiation fog: Large-eddy simulation study at paris-charles de gaulle airport[END_REF].

Chemistry

Meso-NH integrates a chemical module to simulate the evolution of the atmospheric chemical composition [START_REF] Tulet | Description of the mesoscale nonhydrostatic chemistry model and application to a transboundary pollution episode between northern france and southern england[END_REF]. In the present case, the coupled differential chemistry equations are solved using the QSSA (Quasi-Steady-State Approximation [START_REF] Hesstvedt | Quasi-steady-state approximations in air pollution modeling: Comparison of two numerical schemes for oxidant prediction[END_REF]) solver. The chemical scheme, the Generic Reaction Set (GRS - [START_REF] Azzi | An introduction to the generic reaction set photochemical smog mechanism[END_REF]; [START_REF] Venkatram | The development and application of a simplified ozone modeling system (soms)[END_REF]), is a semi-empirical photo-chemical model that allows a simplified representation of the main photo-chemical processes through six reactions :

N O 2 + hν -→ O 3 + N O (R.1) N O + O 3 -→ N O 2 + O 2 (R.2) ROC + hν -→ RP + ROC (R.3) RP + N O -→ N O 2 (R.4) RP + RP -→ RP (R.5) RP + N O 2 -→ SN T OT (R.6)
Only six species are considered allowing a low computational cost : the nitrogen monoxide (NO), the nitrogen dioxide (NO 2 ), the ozone (O 3 ), the reactive organic compounds (ROC), the radicals (RP) and the stable nitrogen products (SNTOT) which include gazeous and non-gazeous species compared to the [START_REF] Azzi | An introduction to the generic reaction set photochemical smog mechanism[END_REF] [START_REF] Sillman | The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments[END_REF]. Reaction R.3 represents the formation of RP through ROC photolysis. As explained by [START_REF] Venkatram | The development and application of a simplified ozone modeling system (soms)[END_REF], the absence of ROC sink allows a continuous formation of RP. Reaction R.4 represents the conversion of NO into NO 2 through the radicals RP, while reaction R.5 is another sink for the radicals. Finally, the conversion of NO 2 into SNTOT is represented by reaction R.6. SNTOT are considered as long-lived products that do not influence the chemistry at timescales lower than 24 hours.

Despite its simplicity, the GRS chemical scheme has been widely implemented in air quality models and has shown good performances with low computational cost [START_REF] Rojas | Global and local sensitivity analysis of urban background ozone modelled with a simplified photochemical scheme[END_REF]. The dry deposition processes are represented through the resistance analogy approach developed by [START_REF] Wesely | Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models[END_REF]. Numerical simulations are run over a fictitious but realistic airport (CAEPport) defined by the ICAO Committee on Aviation Environmental Protection (CAEP). The CAEPport includes a 4200 m runway, terminals, aprons and all the relevant infrastructures for actual operations (Figure 1). The simulated domain is 9 x 9 km 2 with a 10 m horizontal resolution, which enables the simulation of the turbulence in LES mode. On the vertical, 79 levels are defined with a 2 m resolution within the first 30 m Above Ground Level (AGL) and a gradual stretching up to the domain top (2000 m AGL). The CAEPport layout distinguishes asphalt surfaces and buildings (in gray and black respectively in Figure 1) while the rest of the domain is covered with grassland.

Simulation domain

Initialization

The present study does not intend to reproduce a specific situation but rather to better understand the dynamical and chemical processes involved under an academic meteorological situation. 14-hour simulations are run in order to analyze the plume behavior over a daily traffic cycle (0600-2000 UTC) under quiescent wintertime anticyclonic conditions. An idealized initialization is preferred in order to focus on the local mechanisms governing the plume dispersion. The atmosphere is initialized by prescribing horizontally homogeneous vertical profiles of wind, potential temperature and humidity. An initial zero wind profile is prescribed in order to let the local dynamics build up. For potential temperature (θ) and humidity (R V ), nocturnal profiles measured during the Passy-2015 field experiment [START_REF] Paci | The passy-2015 field experiment: wintertime atmospheric dynamics and air quality in a narrow alpine valley[END_REF] are applied (Figure 2). The initial atmosphere is composed of two layers : a lower 1200m- The surface initialization is performed independently for vegetation and urban covers.

Over vegetation, the soil is saturated and the water is distributed according to a liquid/ice ratio of 0.8/0.2. The surface temperature is initialized so as to be close to the equilibrium with the first level atmospheric temperature. A short numerical spin-up is however needed for surface and atmosphere temperatures to entirely balance. Regarding urban covers, an uniform surface temperature of 276 K is prescribed. An initial surface temperature difference (∆T urb-veg ) of 3 K is thus prescribed between vegetation and urban covers in order to represent the effect of the Urban Heat Island (UHI) [START_REF] Oke | The energetic basis of the urban heat island[END_REF][START_REF] Arnfield | Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island[END_REF]. This effect results from the differences in material properties between vegetated and urban covers (albedo, emissivity, permeability, heat capacity) which modify the energy budget and limit the evening cooling over urbanized environments [START_REF] Gaffin | Variations in new york citys urban heat island strength over time and space[END_REF][START_REF] Rinner | Torontos urban heat islandexploring the relationship between land use and surface temperature[END_REF]. ∆T urb-veg also indirectly accounts for the heat fluxes associated with the airport activity [START_REF] Fan | Modeling the impacts of anthropogenic heating on the urban climate of philadelphia: a comparison of implementations in two pbl schemes[END_REF][START_REF] Ryu | Quantitative analysis of factors contributing to urban heat island intensity[END_REF][START_REF] Bohnenstengel | Impact of anthropogenic heat emissions on london's temperatures[END_REF] which starts 3 hours before the actual simulation start according to the ICAO movement journal. The UHI intensity has been widely studied and the reported values cover a large range of intensities as it may reach more than 10 K under cloudless and windless conditions [START_REF] Dupont | Comparison between the atmospheric boundary layer in paris and its rural suburbs during the eclap experiment[END_REF][START_REF] Montávez | A study of the urban heat island of granada[END_REF][START_REF] Bonacquisti | A canopy layer model and its application to rome[END_REF][START_REF] Santamouris | Heat island research in europe: the state of the art[END_REF]. In the present case, the local dynamics and the pollutants dispersion obviously depend on the chosen initial ∆T urb-veg . The extent to which it may influence the plume dispersion will be further discussed in section 5.

The emission inventory

The emission inventory used in the present study includes all the aircraft emissions released over the simulation domain (i.e. during the taxiing, landing and take-off phases)

along with the APU emissions. It is built using the ATMLab (formerly IESTA -Aubry et al. ( 2010)) and the ICAO database.

The ICAO database includes a movement journal representative of a fictive yearly activity associated with the CAEPport. According to the ICAO, this activity corresponds with "a medium-size regional airport". For the present study, the day with the most flights, namely 311 movements, is selected. This remains well below the activity of major international airports which rather have 2000 movements per day according to the Airport Council International (ACI, 2017). Table 1 provides an overview of the main aircraft in operation during the selected day. For each aircraft type, the For each movement, the ICAO database provides the runway access, the start and end times of the movement. For each combination of runway access (entry/exit) point and gate, a default path has been described. Each path is composed by a series of crossing points by considering a constant speed during the taxiing phase and constant deceleration/acceleration before and after the turns. The mass of the aircraft is not mentioned in the database. It has been automatically fixed, using the BADA minimum and maximum masses [START_REF] Nuic | Bada: An advanced aircraft performance model for present and future atm systems[END_REF]. As a rule of thumb, the departing flights are considered as carrying 90 % of their maximum payload and full fuel tanks while arriving ones are allotted 15 % of load mass in excess of that same payload ratio.

The ATMLab then simulates the trajectories of each aircraft at a 1 second time step, according to its BADA performance category; it computes the thrust at each point, which is then translated as engine throttle, with engine characteristics as listed in the ICAO database 1 .

NOx and other species instantaneous emission rates along the path are computed following • 700 g of NOx and 30 g of ROC emitted in 45 minutes of operation for short-haul flights;

• 2400 g of NOx and 160 g of ROC emitted in 75 minutes of operation for long-haul flights.

At the end, the emission inventory gathers NO, NO 2 and ROC emissions in the form of a 4D grid (x, y, z, t) with a spatial resolution of 5m x 5m x 5m and a 1 second time step.

For each time step and for each emission point, the pollutant masses are attributed to the Meso-NH cell with corresponding coordinates.

A time series of the masses of NOx and ROC emitted over the whole simulation domain 1 https://www.easa.europa.eu/domains/environment/icao-aircraft-engine-emissions-databank is presented in Figure 3-a. The masses have been integrated over 10 seconds to improve the readability. The upper black and gray ticks represent the take-off and landing times respectively. Figure 3-a shows that the NOx emissions cannot be correlated with the movement numbers as large peaks occur during low activity periods (see for instance the last NOx peak around 1920 UTC). Rather, these emissions peaks are due to some particularly heavily emitting aircraft at take-off (Table 1 The spatial distribution of the cumulative masses of NO, NO 2 and ROC emitted at the surface are represented in Figure 3-b -c and -d respectively. Their vertical distribution is summarized in Table 2. Large spatial disparities are noticeable as the predominant pollutants evolve according to the flight phases [START_REF] Lelièvre | Air quality and engine emission at paris cdg airport during airpur field campaigns[END_REF][START_REF] Wood | Speciation and chemical evolution of nitrogen oxides in aircraft exhaust near airports[END_REF][START_REF] Stettler | Air quality and public health impacts of uk airports. part i: Emissions[END_REF][START_REF] Yang | Characterization of aircraft emissions and air quality impacts of an international airport[END_REF]. In particular, ROC are mainly released during taxiing phases which are associated with low engine thrust. Largest emissions are therefore mainly observed on the surface (95.2 %) along the runway access roads (Figure 3

Numerical experiments

In the following, the REF simulation refers to the simulation coupled with the previously described emission inventory representative of a daily activity of 311 movements. In addition, a second simulation (DOUB) is presented in order to quantify the plume evolution when the airport activity is doubled. In DOUB, each aircraft movement is repeated with a 5-minute delay thereby representing a daily airport activity of 622 movements. While this DOUB simulation is not necessarily realistic from the operational point of view, it allows us to assess the effects of a traffic increase on local air quality. Apart from the emission inventory, both simulations share the same numerical configuration.

Analysis of the REF simulation

The present section is dedicated to the analysis of the REF simulation. In the absence of large-scale forcing, the local dynamics plays a key role in pollutants dispersion, which is why the dynamical situation is first described.

Dynamical situation

The chosen meteorological conditions are anticyclonic, sunny and windless. Due to radiation, the surface temperature increases during the day while the Mixed Layer (ML) is developing. -h).

An heterogeneous temperature structure is observed with the built part of the airport that remains warmer than the grassland throughout the simulation. The initial surface temperature ∆T urb-veg of 3 K results in a 2 m potential temperature urban-vegetation difference (∆θ 2m ) of 1 K in the early morning (Figure 4-a). Then, ∆θ 2m ranges between 1 and 3 K, the maximum being reached in the early evening (Figure 4-d). This dynamics is consistent with the UHI phenomenon as the airport platform is made of concrete and asphalt covers which absorb and store more radiation than grassland during the day thereby limiting the nighttime cooling above the platform [START_REF] Lu | A laboratory study of the urban heat island in a calm and stably stratified environment. part ii: Velocity field[END_REF]. This process is also responsible for the heterogeneous development of the ML which is on average 50 m deeper above the platform. The horizontal ∆θ 2m is known to drive the establishment of a local UHI-induced breeze [START_REF] Wong | Mesoscale perturbations on airflow in the urban mixing layer[END_REF][START_REF] Lemonsu | Simulation of a summer urban breeze over paris[END_REF][START_REF] Hidalgo | Urban-breeze circulation during the capitoul experiment: numerical simulations[END_REF]. In the present case, this breeze is composed of a near-surface convergent branch towards the aprons, an ascendant branch above the platform and a divergent branch aloft (Figures 4-e,-f,-g).

A more quantitative description of the atmosphere temporal evolution is presented in and 0.9 m.s -1 respectively. These orders of magnitude are in agreement with those reported in the literature [START_REF] Lu | A laboratory study of the urban heat island in a calm and stably stratified environment. part ii: Velocity field[END_REF][START_REF] Hidalgo | Scaling the daytime urban heat island and urban-breeze circulation[END_REF][START_REF] Abbassi | Prediction of pollution dispersion under urban heat island circulation for different atmospheric stratification[END_REF].

Last, Figure 5-d shows instantaneous profiles of TKE horizontally averaged over the entire domain at 1030 UTC and 1430 UTC. A distinction is made between sub-grid TKE sub and resolved TKE res , the former being calculated by the turbulence scheme while the latter is determined according to :

T KE res (x, y, z) = u (x, y, z) 2 + v (x, y, z) 2 + w (x, y, z) 2 (1)
where u , v and w represent the wind speed fluctuations defined as : u (x, y, z) = U (x, y, z)-< U (x, y, z) > XY with <> XY denoting a spatial average over the X and Y directions. These TKE profiles confirm that the simulation can be considered as a LES as more than 80 % of the turbulence is resolved.

The dynamical LES finally provides a realistic idea of the transport and mixing processes involved in this meteorological situation. These processes, along with the photo-chemical activity, are important to capture as they pilot the pollutants dispersion [START_REF] Sarrat | Impact of urban heat island on regional atmospheric pollution[END_REF].

Qualitative study of the pollutants dispersion

Wintertime conditions do not favor the photo-chemical production of ozone which remains on a destruction regime throughout the simulation (not shown). As a consequence, NO 2 is the primary focus of this section due to the harmful health effect of this component. between. This dynamics leads to NO 2 transport within a radius of about 2 km around the airport. A more quantitative characterization of the NO 2 plume is provided in the next section.

Quantification of the NO 2 plume expansion and people exposure

A characterization of the NO 2 plume expansion and of the exposure of workers and passengers is provided hereafter.

Characterization of the plume expansion

The spatial extent of the NO 2 plume at 2 m AGL is characterized by determining the size of plumes exceeding three concentration thresholds; 50, 100 and 200 µg.m -3 , which represent 1/4, 1/2 and 1 times the regulatory threshold respectively. The time series of the plume sizes (S50, S100, S200) are reported in Figure 8, along with their associated mean concentrations (M50, M100, M200). A Gaussian filter is applied to this temporal series and all the following in order to highlight the hourly tendencies. • Accumulation stage (1700-2000 UTC) : sharp increase of S100 and S200 due to the stratification of the atmosphere. This stage is also marked by a sudden increase of M50 which tends towards M100. The combination of these phenomena illustrates an abrupt deterioration of the air quality over the airport platform.

NO 2 concentration around aprons and terminals

Passengers and airport workers spend most of their time around terminals and aprons.

To quantify their exposure, the NO 2 concentrations reached over the sub-domain D T are represented in Figure 1. Minima, deciles and 99th percentile of NO 2 concentrations at 2 m AGL are reported in Figure 9. The 99th percentile is preferred to the maximal concentration which is always one order of magnitude above (not shown). The last concentration percentile (representing 43 Meso-NH grid cells) is therefore discarded for the present analysis. The concentration envelope illustrates a large spatial variability with NO 2 concentrations ranging from 4 µg.m -3 to more than 10000 µg.m -3 over the sub-domain D T . This wide range of values underlines the benefit of high-resolution numerical studies for an accurate characterization of the pollution at the airport scale. Starting from a clean atmosphere, the median NO 2 concentration ranges between 50 and 120 µg.m -3 while the 99th percentile oscillates around 1000 µg.m -3 and reaches 10.000 µg.m -3

after the evening atmosphere stratification. The difference between the 90th and 99th percentiles shows that very high concentrations are confined to a restricted area. In fact, the 99th percentile is mainly driven by APU emissions occurring over the aprons as shown in This quantitative analysis finally shows that the emissions associated with a mediumsize regional airport are responsible for a deterioration of the air quality with NO 2 standards transgressed during several hours over the aprons. On a larger scale, airport emissions contribute to local pollution up to at least 50 µg.m -3 over several square kilometers. In comparison, [START_REF] Carslaw | A short-term intervention study -impact of airport closure due to the eruption of eyjafjallajökull on near-field air quality[END_REF] estimated a 8-14 µg.m -3 contribution from the London Heathrow airport on the NO 2 concentration measured 200 m south of the platform. This value was averaged over a 6-day period during which the meteorological situation evolved, which partly explains their lower contribution estimation.

Sensitivity analysis : impact of the traffic intensity

The purpose of this section is to characterize the evolution of the plume features when the airport activity is doubled by comparing the REF and DOUB simulations. The dynamical situation remains the same in both simulations as the only change concerns the emission inventory (cf. section 2.3). On average over the entire simulation, the surface of the domain concerned by concentrations over 200 µg.m -3 (S200) is higher by a factor 2.3 in the DOUB simulation. A morning local maximum is reached around 0900 UTC but does not exceed 0.3 km 2 (representing 30 x 100 Meso-NH grid cells). This means that the areas exceeding the NO 2 standards do not extend far beyond the emission sources in the DOUB simulation either. At 2000 UTC however, S200 reaches 1.45 km 2 meaning that most of the platform transgresses the air quality standards because of aircraft emissions only. Regarding the surface with more than 100 µg.m -3 (S100), the DOUB/REF size ratio is equal to 4.4 on average over the 14-hour simulation.

Plume expansion

This high factor is driven by the main change occurring between 1000 and 1400 UTC with a maximum plume size of 2 km 2 reached at 1000 UTC. This represents an increase by a factor 19 of S100 compared to the REF simulation thereby highlighting a strong and non-linear influence of the airport activity on the plume size. From 1400 UTC, S100 follows the same tendency as in the REF simulation. At the end of the simulation (2000 UTC), S100 is higher by a factor 1.37 compared to the REF simulation and reaches 1.9 km 2 . Last, the surface of the simulation domain concerned by concentrations over 50 µg.m -3 (S50) is higher by a factor 2.9 on average over the entire simulation. If the S50 maxima are reached at 0940 UTC in both simulations, the next hours are associated with different tendencies. In the REF simulation, S50 gradually decreases between 1000 and 1900 UTC following an average slope of -0.94 km 2 .h -1 . This dilution stage no longer occurs in the DOUB simulation as S50 oscillates in the range 11-15 km 2 between 0940 and 1700 UTC. The DOUB/REF S50 ratio thus gradually increases reaching 6.8 at 1840 UTC. At the end of the simulation, S50 still extends over a surface of 6.7 km 2 that is 3.3 larger than in the REF simulation.

The size of the areas affected by airport pollution finally appears to be largely dependent on the traffic intensity. This dependence is non-linear as a twofold increase of the traffic may lead to a 19 times more extended plume. This result is of prime importance as the number of daily movements used to build the emission inventory, namely 311 and 622 for the REF and DOUB simulations respectively (cf. section 2.3), remains well below the activity of international airports.

Passengers and workers exposure

The quantification of the evolution of passengers and workers exposure is presented in This analysis finally shows that the NO 2 plume intensity does not linearly depend on the airport traffic due to the non-linearity of the photo-chemical activity. This problem is already known as it represents one of the major challenge in developing efficient control strategies to mitigate summertime ozone pollution episodes [START_REF] Finlayson | Atmospheric chemistry of tropospheric ozone formation: scientific and regulatory implications[END_REF][START_REF] Sillman | The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments[END_REF][START_REF] Friedrich | Tropospheric Ozone Abatement: Developing Efficient Strategies for the Reduction of Ozone Precursor Emissions in Europe[END_REF][START_REF] Amann | Reducing global air pollution: the scope for further policy interventions[END_REF]. Similar aspects should also be considered by decision-makers to formulate efficient policies and regulations under wintertime conditions when large amounts of NOx are released, as it is the case over an airport.

Discussion

The present study aims to characterize the pollution structure and intensity reached under an academic wintertime anticyclonic situation by considering a realistic activity over a fictitious regional airport. Large-eddy simulations are run by coupling the atmospheric model Meso-NH with a high-resolution emission inventory. The simulations cannot be validated through a direct measurements comparison as the whole situation is fictive. However, the simulated NO 2 concentrations are comparable with measurements reported in the literature with concentrations around 10 3 µg.m -3 near the sources [START_REF] Lelièvre | Air quality and engine emission at paris cdg airport during airpur field campaigns[END_REF] and 10 1 -10 2 µg.m -3 around [START_REF] Herndon | NO and NO2 emission ratios measured from in-use commercial aircraft during taxi and takeoff[END_REF][START_REF] Carslaw | Near-field commercial aircraft contribution to nitrogen oxides by engine, aircraft type, and airline by individual plume sampling[END_REF]. This concentration range over three orders of magnitude demonstrates that airport-related plumes can have a very heterogeneous structure at the platform scale. Besides, this questions the representativeness of measurements obtained at a single point. In particular, concentrations measured a few hundred meters from runways should be analyzed carefully as they are not necessarily rep-resentative of the pollution levels to which passengers and workers are exposed over aprons and terminals. This result is a clear indicator of the need for both numerical studies and field-experiments with spacialized measurements to improve the understanding of pollution dispersion over airports.

An important result of this study is that the proportion of the domain exceeding the NO 2 regulatory threshold is mainly limited to aprons and runway, except at the end of the simulation where a large portion of the platform is in exceedance. However, premature conclusions should not be drawn as the numerical set-up is designed in order to focus on the airport-related pollution over a single diurnal cycle, starting from near-zero background concentration. In reality, day-to-day accumulation may occur under persistent anticyclonic conditions which are usually associated with the strongest pollution events [START_REF] Wei | Impact of boundary-layer anticyclonic weather system on regional air quality[END_REF][START_REF] Grundstrom | Influence of atmospheric circulation patterns on urban air quality during the winter[END_REF][START_REF] Largeron | Persistent inversion dynamics and wintertime pm10 air pollution in alpine valleys[END_REF]. As a consequence, if simulations were continued over several days, the proportion of the domain transgressing the air quality standards would progressively increase. Besides, the emission inventory used to perform the simulations only accounts for airport-related emissions. In more realistic conditions, the pollutants associated with traffic, industries or cities would be added as most of the airports are located in suburban areas for commodity reasons. As a result, if a 50 µg.m -3 contribution from airport-related emissions to NO 2 levels may appear small over an idealized clean environment, it can no longer be neglected under realistic conditions as it may bring certain areas to transgress the air quality standards. Future contributions could be conducted by progressively increasing the degree of realism of the simulation, i.e. by considering additional processes (large-scale conditions) and emission sources (traffic, nearby cities..). With this step-by-step approach, the relative influence of all the multi-scale processes could be quantified.

The simulated plume behavior obviously depends on the simulation initialization raising Lastly, an initial ∆T urb-veg is prescribed to account for the UHI effect (cf. section 2.1.4). If the simulations were initialized with an homogeneous temperature surface field, the ∆T urb-veg would develop anyway a couple of hours after sunrise because of the land-use difference between the airport platform and the surrounding grassland which generate different surface fluxes partitioning [START_REF] Jusuf | The influence of land use on the urban heat island in singapore[END_REF]. This delay would however reduce the atmospheric dilution volume leading to an overall increase of the NO 2 concentrations.

Regarding the numerical configuration, future improvements could be made. In its current version, Meso-NH does not account for exhaust gas temperature nor exhaust speed.

While this should not have a significant impact on average concentrations, which rather depend on dilution volume and photo-chemical activity, this may induce an overestimation of the concentration maxima as buoyant exhaust plumes may enhance turbulent mixing and dilution near the tailpipe [START_REF] Gamier | Modelling exhaust plume mixing in the near field of an aircraft[END_REF][START_REF] Aloysius | On the Use of Near Field Computational Fluid Dynamics for Improving Airport Related Dispersion Models[END_REF][START_REF] Synylo | Improvement of airport local air quality modeling[END_REF]. In addition, dynamical processes induced by the aircraft motion, such as vortices, may also modify the dilution volume and thus the concentrations [START_REF] Bennett | Lidar observations of aircraft exhaust plumes[END_REF].

Implementing the Meso-NH model to account for these parameters could allow to move the simulations to the next level of realism.

Concerning the chemistry module, the small number of prognostic species and equations of the GRS scheme (cf. section 2.1.2) has the advantage of a low computational cost. However, this equation set does not account for all the chemistry processes involved over an urban pollution plume [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Several hypotheses are made as for instance, aggregating the reactive organic compounds into a single family while their reactivity may change a lot depending on their origin [START_REF] Sillman | The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments[END_REF]. In the future, it would be interesting to use a more complete chemical scheme such as ReLACS [START_REF] Crassier | Development of a reduced chemical scheme for use in mesoscale meteorological models[END_REF][START_REF] Leriche | A cloud chemistry module for the 3-d cloud-resolving mesoscale model meso-nh with application to idealized cases[END_REF] which treats 41 species through 131 photo-chemical reactions. To be relevant, this simulation would require a more detailed speciation of the airport-related emissions. The work of the European Environment Agency (2019) could be used to obtain a finer description of the organic compounds. In addition, a more complete emission inventory would allow the activation of the aerosol module ORILAM [START_REF] Tulet | Orilam, a three-moment lognormal aerosol scheme for mesoscale atmospheric model: Online coupling into the mesonh-c model and validation on the escompte campaign[END_REF] in order to assess the levels of particulate matter which play a central role in airport pollution [START_REF] Hudda | International airport impacts to air quality: size and related properties of large increases in ultrafine particle number concentrations[END_REF][START_REF] Shirmohammadi | Emission rates of particle number, mass and black carbon by the Los Angeles International Airport (LAX) and its impact on air quality in Los Angeles[END_REF][START_REF] Stacey | Measurement of ultrafine particles at airports: A review[END_REF].

Last, the inclusion of Ground Support Units and Ground Service Equipment into the emission inventory would provide a more extensive representation of the airport-related emissions sources. The same numerical set-up could be used again to perform sensitivity analysis in order to quantify the relative contribution of each source to the total airport-related pollution. This approach would allow an identification of the most emitting sources that must be known to formulate efficient regulations for reducing the aviation impact on local air quality.

Conclusion

The contribution of airport-related emissions on local air quality is quantified by performing 14-hour large-eddy simulations with the atmospheric model Meso-NH. An idealized numerical set-up is used in order to isolate the airport pollution. This configuration allows us to characterize the intensity, structure and spatial expansion of the associated plume under an academic wintertime anticyclonic situation. Meso-NH is coupled with an emission inventory providing a realistic activity over a fictitious regional airport. Two simulations are run in order to assess the effects of a traffic increase on local air quality. Starting from a quiescent clean atmosphere, a very heterogeneous plume develops over the platform with NO 2 concentrations in the range 10 1 -10 4 µg.m -3 . Unsurprisingly, the highest concentrations are reached near the emissions sources (runway, aprons) where the NO 2 regulatory standards are transgressed during several hours. Passengers and airport workers who mainly stay around aprons are exposed to median NO 2 concentrations ranging between 50 and 120 µg.m -3 over the 14-hour cycle while local maxima around 1000 µg.m -3 persist near the APU. On a larger scale, the pollution plume extends beyond the platform due to the establishment of the local dynamics (UHI-induced breeze and convective mixing developed during the daytime period). More precisely, the airport-related emissions contribute to NO 2 concentrations up to at least 50 µg.m -3 within a region ranging from 1 to 9 km 2 around the airport. Added to regional or suburbans NO 2 levels, this airport contribution may bring the surrounding areas to transgress the air quality standards under more realistic conditions, as it represents 1/4 of the NO 2 regulatory threshold. The traffic intensity has a noticeable impact on the plume whose spatial expansion increases in a non-linear way. By doubling the airport activity, the surface concerned by concentrations over 50 µg.m -3 is higher by a factor 2.9 on average over the entire simulation and reaches a maximum of 15 km 2 . A less pronounced impact on pollutant concentrations is observed as a twofold augmentation in traffic results in a less than twofold NO 2 concentrations augmentation. This results from the non-linearity of the photo-chemical processes whose relative contribution evolves depending on the atmospheric chemical composition. The present study finally provides a fine-scale understanding of the dynamical and chemical processes driving the spatio-temporal evolution of an airport pollution plume. These results may help decision-makers to formulate efficient policies and regulations in order to reduce the impact of aviation on local air quality.

Figure 1 :

 1 Figure 1: Simulation domain with asphalt surfaces in gray, buildings and tanks in black. The red box materializes the sub-domain D T used to quantify the people exposure in section 3.3.2.

Figure 2 :

 2 Figure 2: Initial vertical profiles of potential temperature and water mixing ratio represented by solid and dashed lines respectively.

  the Boeing Fuel Flow Method 2 (DuBois and Paynter, 2006). The recommendations from the UK Department for Transport (2006) are used to split NOx emissions into their NO and NO 2 components, while unburned hydrocarbons are prescribed as the ROC species of the GRS chemical scheme. The emissions are distributed in proportion to the time passed in each cell, thus forming a series of adjacent cells. APU emissions over aprons are integrated following the ICAO recommendations (ICAO, 2012) :

Figure 3 :

 3 Figure 3: (a) Temporal series of cumulative masses of NOx and ROC emitted over the entire numerical domain (the temporal bars have a 10 second width) and (x,y) diagrams of cumulative pollutant masses released at the surface only and integrated over the 0600-2000 UTC period for (b) NO, (c) NO 2 and (d) ROC.

  -d). NO 2 is also mainly emitted on the surface (89 %) where the highest emissions occur along the runway and over aprons on either side of the terminal where APU are located (Figure3-c). Conversely, NO emissions are more evenly distributed vertically since NO dominates under high engine thrust, i.e. during take-off and climbing. At the surface, high emissions are thus logically located along the runway (Figure3-b).

Figure 4 :

 4 Figure 4: Diagrams of potential temperature and wind vectors extracted (a,b,c,d) horizontally at 2 m AGL for temperature and averaged over the first 20 m AGL for wind field and (e,f,g,h) vertically at X = 4.5 km (the gray rectangle represents the airport location). Arrow orientation and length indicate the wind direction and intensity. The white boxes in panel (a) represent the sub-domains D 1 and D 2 used to calculate the averaged vertical profiles of horizontal and vertical wind-speed displayed in Figure 5.

Figure 4

 4 Figure4shows (x,y) and (y,z) diagrams of the simulated potential temperature and wind vectors fields, hourly averaged over four 60-minute periods. Horizontally, the temperature is extracted at 2 m AGL while the wind is vertically averaged over the first 20 m AGL (Figure4-a,-b,-c,-d). The vertical fields of wind and potential temperature are extracted in

Figure 5

 5 Figure 5 which gathers hourly averaged vertical profiles of (a) the potential temperature horizontally averaged over the entire domain; (b) the structure of the urban breeze averaged over the sub-domain D 1 shown in Figure 4-a, with positive (respectively negative) intensities representing the convergent southward (respectively divergent northward) branch and (c) the vertical wind-speed averaged over the ascendant branch within the sub-domain D 2 displayed in Figure 4-a.

Figure 5 :

 5 Figure 5: Hourly averaged vertical profiles of (a) potential temperature horizontally averaged over the entire domain, (b) urban breeze structure -with positive and negative intensities associated with convergent and divergent branches respectively -averaged over the sub-domain D 1 shown in Figure 4-a, (c) vertical wind-speed averaged over the ascendant branch within the sub-domain D 2 displayed in Figure 4-a. (d) Instantaneous profiles of resolved (continuous) and sub-grid (dashed) TKE averaged over the entire domain at 1030 UTC (brown) and 1430 UTC (green).

Figure 6

 6 Figure 6 represents hourly averaged (x,y) diagrams of NO 2 concentrations at 2 m AGL. During the first simulation hours, the plume structure mainly relies on emissions location with high NO 2 concentrations corresponding with the main NO 2 sources, as shown in Figure 3. The air quality progressively deteriorates over aprons and runway as dispersion conditions are weak. From 0900 UTC, pollution extends over regions without emission source. The origin of this phenomenon is illustrated in Figure 7 through (y,z) diagrams of hourly averaged NO 2 concentrations and wind vectors extracted at X = 4.5 km. During the first hours, NO 2 is vertically advected above the platform by the ascendant branch of the breeze, leading to the formation of an elevated pollution plume around 100 m AGL (Figure 7-a). At that

Figure 6 :

 6 Figure 6: Hourly averaged (x,y) diagrams of NO 2 concentrations at 2 m AGL.

Figure 7 :

 7 Figure 7: Hourly averaged (y,z) diagrams of NO 2 concentration and wind vectors extracted at X = 4.5 km. Arrows orientation and length indicate the wind direction and intensity

Figure 8

 8 Figure8illustrates the horizontal expansion of the airport pollution, which is initially limited to the platform and then progressively extends beyond. In particular, the area within which the concentration exceeds 50 µg.m -3 (S50) reaches a maximum of 9 km 2 at 0940 UTC meaning that the airport pollution contributes up to at least 1/4 of the NO 2 regulatory threshold over this area. The plumes exceeding 100 and 200 µg.m -3 reach 1.4 and 1 km 2 respectively at 2000 UTC (1 km 2 representing 100 x 100 Meso-NH grid cells). Their associated mean concentrations largely transgress the air quality standards. In particular, M200 ranges between 600 at 1100 µg.m -3 during the entire simulation. Although confined around the aprons, these concentrations represent a damaging health hazard for airport staff working close to the emissions sources.

Figure 8 :

 8 Figure 8: Time series of (a) the size of the NO 2 plumes with concentrations above 50, 100 and 200 µg.m -3 and (b) the associated mean concentrations. The 200 µg.m -3 regulatory threshold is indicated by the black dashed line in panel -b.

Figure 8

 8 Figure 8 also allows the decomposition of the plume behavior in four main stages that can be related to the atmospheric dynamics described in section 3.1 :

Figure 9 :

 9 Figure 9: Time series of minima, deciles and 99th percentile of NO 2 concentration determined within the sub-domain D T displayed in Figure 1. The thick lines represent the minima, mediane and 99th percentile while the thin lines are associated with concentration deciles.

Figure 3 .

 3 Figure 3. During most of the simulation, these emissions shots are quickly dispersed as 90 % of the sub-domain D T remains below 200 µg.m -3 . From 1800 UTC, however, the 90th percentile starts increasing and reaches the regulatory threshold 40 minutes later. This dynamics is a clear illustration of the shift towards the accumulation regime due to the atmosphere stabilization. At the end of the simulation (2000 UTC), more than 30 % of the sub-domain D T exceeds the 200 µg.m -3 limit because of aircraft-related emissions only.

Figure 10 :

 10 Figure 10: Time series of (a) the sizes of the NO 2 plumes exceeding 50, 100 and 200 µg.m -3 in the DOUB (continuous lines) and REF (dotted lines) simulations and (b) the DOUB/REF ratios of plume sizes.

Figure 11

 11 Figure 11-a. As in section 3.3.2, time series of minima, deciles and 99th percentile of NO 2 concentrations are calculated for the DOUB simulation and are represented in brown in Figure 11-a. These values are determined at 2 m AGL over the sub-domain D T displayed in Figure 1. The blue envelope recalls the Minima-99thPercentile concentrations obtained in the REF simulation (Figure 9).

Figure 11 :

 11 Figure 11: Time series of (a) minima, deciles and 99th percentile of NO 2 concentration determined over the sub-domain D T (Figure 1) in the DOUB simulation (brown) and (b) DOUB/REF ratios for minima, median and 99th percentile of NO 2 concentration. The blue envelope displayed in panel -a recalls the Minima-99thPercentile concentrations range obtained in the REF simulation (cf. Figure 9).

Figure 12 :

 12 Figure 12: (x,y) diagrams of (a,e) NO 2 , (b,f) NO, (c,g) O 3 concentrations and (d,h) NO/NO 2 concentration ratio averaged over the 1300-1400 UTC period. The fields are extracted at 2 m AGL for the REF and DOUB simulations, upper and lower panels respectively.

  Figure6. As a consequence, non-zero background wind conditions may be associated with a reduction of the vertical plume spread and a change in the horizontal structure of the plume which may become longer along the prevailing wind direction. Note that the structure of the UHI-induced breeze -and thus the structure of the plume -may also be disturbed by the interactions with others UHI-induced breezes associated with the surrounding cities as recently shown by[START_REF] Wang | Interacting urban heat island circulations as affected by weak background wind[END_REF].

Table 1 :

 1 Summary table of the main aircraft in operation during the selected day. The occurrence number and the NOx mass emitted over the simulation domain are provided for each aircraft type.

	Type	E145 B735 AT72 E170 B734 AT45 A319 MD11 A320 CRJ2 B733
	Number	62	32	32	29	20	20	17	14	14	8	7
	NOx (kg)	3	9	2	3	10	2	11	64	13	2	8

Table includes the number of occurrence and the NOx mass released during take-off. These data highlight a large variability in NOx emissions, which can change by a factor 30 depending on aircraft type.

Table 2 :

 2 ). Vertical distribution of the masses emitted over the entire simulation for NO, NO 2 and ROC along with the percentage of mass emitted at the surface.

	NO NO 2 ROC

Acknowledgments

This paper and the research behind was achieved through the financial support provided by the FCS-STAE foundation. The numerical configuration is partly based on the outcomes of the MOSIQAA project funded by DGAC. Numerical simulations were performed using HPC resources from CALMIP (Grant 2019-P19032) and GENCI (Grant 2020-A0080107584).

We thank E. Montreuil for his help in the creation of the emission inventory. We are also grateful to the Meso-NH support team for assistance with the model.