domain: sde spi

text:
E Chevet

O Boiron

F Anselmet

Modelling of air pollution due to marine traffic in Mediterranean coastal cities

Keywords: Atmospheric Pollution, Ship Traffic, Numerical Weather Prediction, Chemical Emissions

. For ship traffic, AIS data provides position, speed or time at berth of any given boat as well as its main features. Methodology to estimate ship emissions is derived from both EMEP/EEA [6] and EPA [8] reports. The relative difference in concentration between a situation with and without ship traffic allows us to evaluate the influence of ships on air quality. The effect of seasonality is also analyzed, by running 4 simulations of 72 hours for each season. When compared to the values presented in Viana et al.

[22], the model seems to underestimate the contribution of the shipping sector to urban pollution for gas species, estimated around 1% for SO2, NO2 and O3. However, PM10 and PM2.5 contribution levels are consistent with previous studies, with values ranging from 3.5% to 12.8%.

Introduction

The seaways are more and more frequented, both for development of international trade and for tourism. Over the past 40 years, world seaborne trade and maritime tourism in cruises have grown by an average of +3% [START_REF] Sirimanne | Review of maritime transport[END_REF] and +7% each year respectively. Besides, 70% of the emissions related to maritime transport occur within 400 km of the coast. In fact, most cruise ships continue to run their engines at dockside in port, directly impacting the health of populations near the port area. It has been estimated that fine particles emitted by ships close to the coast are responsible for 60000 premature deaths per year [START_REF] Corbett | Mortality from Ship Emissions: A Global Assessment[END_REF]. Although measures have been established by the International Maritime Organization (IMO) to regulate the pollutant content of the fuels consumed, they remain very rich in sulfur oxides (SOx), nitrogen oxides (NOx) and fine particles (PM10 and PM2.5). Since 2020, ship fuels are limited to a mass sulphur content of 0.5% in the Mediterranean, compared to 0.1% in Northern Europe, which is still 1000 times higher than in the diesel for cars. The proximity of large ports to densely populated areas makes these emissions a real threat to the health of the many residents of these port cities. Controlling pollution from maritime transport in port cities is therefore of utmost importance, hence the significance of building a model capable of accurately estimating the contribution of port activity to air pollution based on complete and reliable data. Concerning the data on ship emissions, the "bottom-up" approach has been adopted. Indeed, the "top-down" method consisting in estimating the emissions of the ships from the total fuel consumption was found to be less efficient [START_REF] Miola | Estimating air emissions from ships: Meta-analysis of modelling approaches and available data sources[END_REF]. Traffic data are usually derived from the Automatic Identification System (AIS), which provides real-time information for all sorts of ships worldwide. Given the type, the size and the operating state of a single boat, it is possible to estimate its proper emission. There are two main methods to estimate the impact of anthropogenic emissions of a sector on air quality. The first method consists in identifying by statistical methods the sources of pollutant emissions based on measurements from sensors. This approach, called Receptor Modelling, is frequently used to estimate Figure 1: Map of the study domain the sources of fine particles and aerosols [START_REF] Viana | Source apportionment of particulate matter in Europe: A review of methods and results[END_REF]x. However, it does not account for secondary pollutants, those resulting from chemical transformations and reactions. One alternative is the use of air quality models (AQMs), which provide an effective response to this problem while including these secondary components. Various AQMs have been applied worldwide to determine the impact of marine traffic on air quality: CALPUFF in Napoli [START_REF] Murena | Impact on air quality of cruise ship emissions in Naples, Italy[END_REF]; TAPM in Hamburg [START_REF] Ramacher | Contributions of traffic and shipping emissions to city-scale NOx and PM2.5 exposure in Hamburg[END_REF]; CAMx in Europe [START_REF] Jiang | Changes in ozone and PM2.5 in Europe during the period of 1990-2030: Role of reductions in land and ship emissions[END_REF] etc... WRF-Chem has been utilized several times to determine the impact of maritime traffic on air pollution and has proven to be very efficient for that purpose (Marelle et al. [START_REF] Marelle | Air quality and radiative impacts of Arctic shipping emissions in the summertime in northern Norway: from the local to the regional scale[END_REF],Wang et al. [START_REF] Wang | Effect of ship emissions on O3 in the Yangtze River Delta region of China: Analysis of WRF-Chem modeling[END_REF],Chen et al. [START_REF] Chen | Contribution of ship emissions to the concentration of PM2.5: A comprehensive study using AIS data and WRF/Chem model in Bohai Rim Region, China[END_REF]). These studies are regional with a horizontal resolution ranging from 3 to 15 km. While the port of Marseille (GPMM) has the highest number of calls in France, few studies are focusing on the impact of its activity on the air quality of the city. Project CAIMANS has produced a detailed report in 2015 in which the main Mediterranean ports, including GPMM, have been compared regarding their impact on air pollution [START_REF] Piga | Air quality impact and green house gases assessment for cruise and passenger ships[END_REF]. However, this project focuses on annual analysis, and therefore does not capture high emission events. The aim of this work is to quantify the contribution of ship traffic on the air pollution of Marseille and its surroundings using the fully coupled chemistry model WRF-Chem. Main primary and secondary pollutants are analysed, including SO2, NO2, PM2.5, PM10, and O3. The "bottom-up" method is applied using AIS data in order to define a scenario of high occupancy of the harbor. Seasonal variations and their impact on local meteorology are then investigated by running the same scenario over different periods of 72 hours.

Methodology

Study area

The studied area is centered on Marseille, and includes the entire port of Marseille-Fos as represented in fig 1. The port is divided into two basins. The EAST basin, located in the city of Marseille, welcomes cargo ships, but especially pleasure boats, cruise ships and ferries. The WEST basin constitutes the industrial port area of Fos-sur-Mer to the WEST of Marseille. It is mainly used by vessels specialized in the transport of petroleum chemicals and container ships. The domain of interest (DOI) extends from 4.80°E to 5.80°E and from 42.89°N to 43.61°N, i.e. about 80*80 km 2 . A sub-domain in blue is defined post-simulation to focus the analyses on Marseille.

Ship emissions

Emissions from ships are mainly based on the methodology of the European Environment Agency [START_REF]Air pollutant emission inventory guidebook[END_REF], with some updated tables taken from the EPA [START_REF] Epa | Current Methodologies in Preparing Mobile Source Port-Related Emission Inventories: Final Report[END_REF] report. The method evaluates vessel emissions, based on two characteristics: the gross tonnage (GT), and the boat type. Indeed, for each type was defined laws were defined to determine the nominal power of the vessel given the GT. From these data it is possible to compute emissions according to the following formula:

E i,b,m,p = e P b * LF e,b,p * EF i,e,m /S
with E i,b,m,p the emission flux of the pollutant i from ship type b (Ro Ro Cargo; Bulk Carrier; Container; Passenger; General Cargo; Tug; Tanker) consuming fuel type m (Residual Oil, RO; Marine Diesel Oil, MDO; Marine Gas Oil, MGO) during phase p (cruise; manoeuvring; hotelling). P b is the nominal power, LF e,b,p is the load factor for engine type e (Propulsion; Auxiliary; Boiler), EF i,e,m is the emission factor, and S the surface of a grid cell. This methodology is therefore applied to every boat included in the considered traffic scenario, which includes 42 boats at berth and 8 arrivals and departures per day with a GT ranging from 6680 to 157000. The height of naval emissions is defined at the second vertical level, i.e. 50m above the ground. Only cruise and hotelling phases are considered for a matter of simplicity.

Model setup

The model WRF-Chem (Weather Research and Forecasting Chemical model, version 4.2) has been applied in this study to evaluate the impact of maritime traffic on air pollution over Marseille and its surroundings. WRF-chem does online calculation of dynamical inputs (winds, temperature, boundary layer, and clouds), transport (advective, convective, and diffusive), dry deposition [START_REF] Wesely | Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models[END_REF], gas-phase chemistry, radiation, and photolysis rates [START_REF] Tie | Effect of clouds on photolysis and oxidants in the troposphere[END_REF]. The following physical schemes are used in this study: the RRTM radiation scheme [START_REF] Mlawer | Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave[END_REF], the WSM-6 graupel microphysics scheme [START_REF] Huang | Parallel GPU architecture framework for the WRF Single Moment 6-class microphysics scheme[END_REF], the Bougeault and Lacarrère [START_REF] Bougeault | Parameterization of orography-induced turbulence in a mesobeta-scale model[END_REF] turbulent kinetic energy (TKE) planetary boundary layer (PBL) scheme, the Noah land surface model and the MM5 similarity surface layer scheme [START_REF] Chen | Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity[END_REF]. As for chemistry, the chosen model MOZCART is a combination of two models: MOZART-4 as a gas-phase mechanisms and chemical transport model [START_REF] Emmons | Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)[END_REF], and GOCART as an aerosol transport model [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF]. Simulations are run with a three-level nesting, with a final horizontal resolution of 0.8*0.8 km 2 on the DOI. Meteorological inputs for initial and boundary conditions are derived from the National Centers of Atmospheric Research (NCAR) Final Analysis (GDAS/FNL) data, which were available at a 0.25°×0.25°resolution and a temporal resolution of 6 h. Anthropogenic emissions are taken from the EDGAR/HTAP annual emission inventory, to which hourly coefficients from Crippa et al. [START_REF] Crippa | High resolution temporal profiles in the Emissions Database for Global Atmospheric Research[END_REF] were applied. Model of Emission Gases and Aerosols from Nature (MEGAN) allows the integration of biogenic isoprene emissions into WRF-Chem with a 1 km 2 spatial resolution [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF]. Two 72-hour time slots per season are considered as presented in table 1. One for which the average wind is low and the other corresponding to an average wind with respect to the observations of the given season. These latter are taken from the meteorological station of Marignane for the year 2021 with a 3-hour time interval. For each time slot, two simulations, with and without ship emissions, are performed with the same conditions otherwise. The comparison of the two outputs enables to calculate the contribution of ship emissions on the concentration fields of the different species analyzed here.

Results and Discussion

The difference between the output fields of the simulations with and without boat emission allowed us to determine their contributions for the different pollutants. Table 2 presents the contribution for each pollutants. %_Max is the spatial maximum observed over the time-averaged contribution field whereas %_Avg_Mrs is the mean contribution over the rectangular sub-domain focused on Marseille represented in Fig. 1. Calculations are done on the first simulated vertical domain only. Ships seem to play a greater role in aerosol concentration than in gas-phase species concentration. Indeed, boats are responsible for 3.5 to 12.8% of the concentration of particulate matter against about 1% for SO2, NO2 and ozone. This is partly due to the stack height of boats, emitting at 50m above ground. Gaseous species are much more volatile than aerosols and will tend to stay at the same level, while aerosols are subject to gravitational settling. PM2.5 and PM10 ship contribution levels are consistent with the results summarized in Viana et al. [START_REF] Viana | Impact of maritime transport emissions on coastal air quality in Europe[END_REF], (1-7% and 1-14% for PM10 and PM2.5 respectivly). However, NO2 levels are lower (∼1%) than those presented in the study (7-24%). These low levels can also be explained by the fact that emissions during manoeuvring are not considered in the estimation of ship emission. In the CAIMANs report [START_REF] Piga | Air quality impact and green house gases assessment for cruise and passenger ships[END_REF], emissions from manoeuvring phase are found to be much higher than those from hosteling phase, especially for SO2. For all time slots and for every species, we note that the mean contribution of boats is higher over Marseille than over the whole domain. For instance, as it is for PM10, pollution from boats stands for 0.69 µg.m -3 on the domain against 2.83 µg.m -3 over Marseille on average for time slot 20210422. Regarding seasonal variations, the differences from one season to another are not significant enough to conclude with such a small sample of days. However, it seems that the contribution of boats on ozone is higher in winter, when the nebulosity is at its highest level.

Conclusions and future research

Air pollution from ships is still a major problem in Mediterranean coastal cities, since maritime traffic is constantly increasing and restrictions on the fuel used in this area are not sufficient. While studies on the impacts of ships are becoming more and more present worldwide, few have been conducted on the city of Marseille. In this work, we built a city-scale model using WRF-Chem that allows us to estimate the contribution of boats to air pollution for the major pollutants. The results showed higher contribution for PM10 and PM2.5, reaching about 13% in the worst case. However, the model seems to underestimate the contribution to gas-phase species concentration. Emissions from maneuvering phases are being considered for inclusion to correct this effect in the future. We also intend to analyze the influence of onshore power supply and a change in fuel quality. Finally, we are planning a full year study to better understand the effects of seasonality. Centre de Calcul Intensif d'Aix-Marseille is acknowledged for granting access to its high performance computing resources.

Figure 2

 2 shows time-averaged contribution of boats in PM 2.5 for time slots corresponding to typical wind speeds observed in each season. The areas with the highest contribution (between 35 and 50%) are located close to the docked vessels' positions. Plumes oriented in the direction of the prevailing winds are clearly observed, especially for time slots 20210828 and 20211106 where the wind direction was less fluctuating.

Figure 2 :

 2 Figure 2: Contribution of ships to mean PM2.5 in %. 20210123 (a); 20210503 (b); 20210828 (c); 20211106 (d)

Table 1 :

 1 Weather conditions for each time slot over 2021

		Start/end dates Wind speed Wind direction Temperature Nebulosity
			m.s -1	•	K	Octas
	Winter 03 -02/03 -05	1.48	135	282.5	4.04
		01 -23/01 -26	5.68	315	281.4	3.42
	Spring	04 -22/04 -25	2.71	180	288.1	1.16
		05 -03/05 -06	4.35	292.5	288.8	1.21
	Summer 07 -19/07 -22	2.68	315	300.0	0.91
		08 -28/08 -31	5.40	315	295.0	0.83
	Fall	11 -19/11 -22	1.19	135	283.5	1.79
		11 -06/11 -09	3.98	0	283.8	2.00

Table 2 :

 2 Contribution of ships to pollutants concentration

	Time slots		PM2.5	PM10	SO2	O3	NO2
			µg.m -3 µg.m -3 µg.m -3 µg.m -3 µg.m -3
	03 -02/03 -05	%_Max	19.5%	21.8%	6.0%	9.3%	3.1%
		%_Avg_Mrs	3.5%	4.2%	0.6%	0.9%	1.1%
	01 -23/01 -26	%_Max	35.2%	37.7%	10.4%	2.7%	5.0%
		%_Avg_Mrs	5.5%	6.2%	0.8%	1.3%	1.0%
	04 -22/04 -25	%_Max	16.5%	18.3%	3.3%	5.0%	2.9%
		%_Avg_Mrs	6.1%	7.5%	0.9%	0.4%	1.1%
	05 -03/05 -06	%_Max	29.2%	31.5%	5.6%	6.1%	3.2%
		%_Avg_Mrs	6.4%	7.4%	0.7%	0.5%	0.7%
	07 -19/07 -22	%_Max	21.1%	24.2%	2.7%	3.5%	2.1%
		%_Avg_Mrs	10.7%	12.8%	1.0%	-0.1%	0.8%
	08 -28/08 -31	%_Max	45.4%	50.7%	5.5%	7.2%	2.4%
		%_Avg_Mrs	5.9%	7.0%	0.7%	6.0e-3%	0.5%
	11 -19/11 -22	%_Max	14.5%	15.0%	3.4%	10.3%	5.1%
		%_Avg_Mrs	7.55%	9.3%	1.0%	1.1%	1.5%
	11 -06/11 -09	%_Max	40.6%	43.5%	8.6%	3.4%	3.0%
		%_Avg_Mrs	4.6%	5.7%	0.8%	0.34%	0.5%