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1 Introduction

Shipping activity and freight transport via ships are growing, and previous studies have shown that the relative potential impact from shipping to total air pollution will also increase (Brandt et al., 2013). Once in the atmosphere, these emissions are transported over several hundreds of kilometers, with 70 % of shipping emissions occurring less than 400 km from the coast (Eyring et al., 2010; Endresen et al., 2003). Several previous studies have pointed out the negative effect of shipping emissions on the concentration of air pollutants, playing a role as greenhouse gases, impacting human health, or contributing to acidification and eutrophication (Tysro and Berge, 1997; Corbett and Fischbeck, 1997; Corbett et al., 1999). An overview over the current knowledge of effects of shipping on air quality and the human health worldwide is given in a review by Contini et al. (2021). Nevertheless, maritime transport plays a vital role in the international trade of goods worldwide as well as in the European Union (EU). The Eurostat Press Office (2016) stated that for 2015, the value of EU trade of goods with non-EU countries transported by the sea was approximately 51 % of EU traded goods. The Mediterranean Sea serves as both the primary shipping route between Europe and East Asia and the principal route for short sea shipping within Europe. It is the region in Europe with maximal impact from shipping emissions to gaseous pollutants, in addition to the North Sea (Viana et al., 2014).

Additionally, as one of the fastest growing sources of greenhouse gas emissions, shipping emissions directly result in health problems and have adverse effects on ecosystems (Brandt et al., 2013). The wide range of gaseous pollutants, such as nitrogen oxides ( ${NO}_{x} = {NO}_{2} + NO$ ), coming from shipping emissions have negative impacts by forming smog and acid rain and contribute to eutrophication (Jägerbrand et al., 2019; Brandt et al., 2013; Karl et al., 2019a; Matthias et al., 2010).

Moreover, NO $_{x}$ , as a primary pollutant, plays an important role in the formation of O $_{3}$ and in the deposition of reactive nitrogen compounds (Eyring et al., 2010). The oxidation of VOCs (volatile organic compounds) produces ozone in the troposphere when NO $_{x}$ and sunlight are present. O $_{3}$ can inflame and damage the respiratory system, make the lungs more susceptible to infection, and intensify lung diseases (EPA, 2021). Although it is not directly emitted, O $_{3}$ is an important compound in photochemistry. Especially in the Mediterranean Sea during summer, when radiation is high, the contribution of shipping emissions to mean surface O $_{3}$ concentrations can be significant (Aksoyoglu et al., 2016).

Atmospheric nitrogen deposition mainly comes from agricultural activities and combustion processes such as those in shipping (Aksoyoglu et al., 2016). This increase in bioavailable nitrogen deposition causes eutrophication (Jägerbrand et al., 2019). The deposition of O $_{3}$ affects a plant's stomata, damages plants, changes water and carbon cycling, and reduces crop yields (Clifton et al., 2020).

Chemistry transport models (CTMs) can be applied to simulate the transport of air pollutants as well as chemical transformation and deposition. These models can be used at different scales, depending on the domain they cover and the question to be answered.

Although shipping emissions have a significant impact on air pollution by NO $_{2}$ in the Mediterranean Sea (Marmer and Langmann, 2005), few regional-scale chemistry transport modeling studies have focused on this domain. A literature review study focusing on the assessment of the impacts of shipping emissions on air quality in European coastal areas by Viana et al. (2014) showed that studies regarding shipping emissions in the Mediterranean Sea emphasize PM levels and their chemical composition instead of gaseous pollutants. Marmer and Langmann (2005) investigated the Mediterranean Sea but on a larger scale or without the comparison of different CTMs. Other studies focus on smaller domains over the Iberian Peninsula (Baldasano et al., 2011; Nunes et al., 2020), the eastern part of the Mediterranean Sea with the Arabian Peninsula (Večeřa et al., 2008; Tadic et al., 2020; Celik et al., 2020; Friedrich et al., 2021), or the urban scale and harbor cities (Schembari et al., 2012; Donateo et al., 2014; Prati et al., 2015). However, none of these studies modeled the potential impact of ships on a regional scale with a subsequent model comparison of different CTMs. A comparison of results of regional-scale chemistry transport models has been performed for the Baltic Sea and for all of Europe (Karl et al., 2019b; Im et al., 2015a) but not exclusively for the western Mediterranean region.

Dry deposition is a substantial sink for atmospheric pollutants. Furthermore, it determines the net flux of pollutants to the Earth's surface (Galmarini et al., 2021). Accurate estimates of dry deposition are required for reliable predictions of atmospheric concentrations, since it is an important loss process scaling with concentrations close to the ground (Emerson et al., 2020; Vivanco et al., 2018). NO $_{2}$ deposition contributes to eutrophication, followed by biodiversity loss, whereas O $_{3}$ dry deposition injures plant tissues and reduces plant productivity (Vivanco et al., 2018; Clifton et al., 2020). The deposition of N and S was investigated in previous studies (i.e., Vivanco et al., 2018; Jutterström et al., 2021; Galmarini et al., 2021). Nevertheless, few studies have performed model intercomparison for dry deposition; thus far, none of the studies have focused on ship impact over the western part of the Mediterranean Sea. Comparing the dry-deposition mechanisms of different models is essential since these mechanisms are unique for each model. In Galmarini et al. (2021), deposition schemes of different models were compared, including LOTOS-EUROS and CMAQ, which are also part of the present study. They showed, e.g., differences in surface resistance calculation and deposition pathways. LOTOS-EUROS uses a single deposition pathway to soil. In comparison, CMAQ uses two deposition pathways for deposition to soil (one for vegetation-covered and one for bare soil).

Additionally, another important factor is the land use–land cover (LUCL), on which dry deposition strongly depends but which is unique in each model. This was also stated by Vivanco et al. (2018), explaining that even if models apply similar algorithms in their deposition schemes, they may use different land use or leaf index area data. Thus, mainly over land areas, differences in model simulations are to be expected. A similar mechanism and model results for dry deposition is expected over water and therefore over most of the considered domain in the present study.

The Ship Traffic Emission Assessment Model (STEAM) has been previously applied to evaluate shipping emissions in different regions, such as the North Sea or Baltic Sea (Jalkanen et al., 2009; Jonson et al., 2015; Aulinger et al., 2016; Barregard et al., 2019) or the Iberian Peninsula (Nunes et al., 2020) as well as in European (Jalkanen et al., 2016) and global regions (Johansson et al., 2017). However, the model has not been previously used in a study focusing entirely on the western Mediterranean Sea region.

In addition, the Mediterranean Sea is not yet an ECA (Emission Control Area). The contracting parties of the Barcelona Convention agreed to designate the Mediterranean Sea as an Emission Control Area for Sulfur emissions (MedECA) by 2025. Nevertheless, although SO $_{2}$ emissions must be reduced by 50 % to 80 % by 2030, NO $_{x}$ emissions from ships will grow without further control and likely exceed emissions from land-based sources in the European Union after 2030 (Cofala et al., 2018). Furthermore, the current state of air pollution is calculated to have a basis for investigating the effects of additional legislation. It is important to simulate the potential impact of ships on several air pollutants to show the impact of ships in a larger area.

The Horizon 2020 SCIPPER project (Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations) aims to determine how existing regulations ensure compliance with the legislation on emissions to air from ships. One part of this project was to focus on CTMs and their possible supportive effects in the monitoring of the compliance of threshold levels.

The present study compares and evaluates five different CTMs concerning their predictions of the dispersion and transformation of air pollutants. The main focus of this study is to compare the results of model simulations regarding the potential ship impact on atmospheric concentrations and dry deposition of NO $_{2}$ and O $_{3}$ . Using this comparison, important differences in the photochemical processing between the CTMs and the balance of photochemistry in the models focusing on shipping will be highlighted. Furthermore, the model performance was quantified by comparing the simulated data to the measured data of air pollutants at background stations in coastal areas of the Mediterranean Sea. The performance of the models was compared based on statistical indicators.

By using five different CTMs in this part of the SCIPPER project, a more robust estimate of the potential ship impact on the air pollution can be given. To date, the present study is the first multimodel study to compare potential ship impacts on five regional-scale CTMs in the Mediterranean Sea.

2 Materials and methods

2.1 Models

Five different regional-scale CTMs were used for this study, run by four institutions: CAMx (Comprehensive Air Quality Model with Extensions) and CHIMERE by AtmoSud, CMAQ by the Helmoltz Centre Hereon, EMEP (European Monitoring and Evaluation Programme) by the IVL Swedish Environmental Research Institute and LOTOS-EUROS by the TNO Netherlands Organization for applied scientific research.

The goal was to have a model setup that is as similar as possible for all models to receive comparable simulations. As a base, an inner and outer domain with a grid resolution were established. Additionally, the emissions were provided for 1 year. The method for calculating the potential ship impact was of particular importance in the present study.

An overview of the input data is shown in Table 1. Input data were the same for shipping emissions using STEAM (version 3.3.0; Jalkanen et al., 2009, 2012; Johansson et al., 2013, 2017), land-based emissions (CAMS-REG, v2.0) and projection (WGS84_lonlat), domain (Mediterranean Sea), resolution ( $0.1^{\circ} \times 0.1^{\circ}$ , 12 km $\times$ 12 km) and the modeled year (2015). Input data were different for meteorological input data and boundary and initial conditions because the CTMs used their standard setup.

Table 1

Main model parameters and input data for the five chemical transport models.

Model parameter	CAMx	CHIMERE	CMAQ	EMEP	LOTOS-EUROS
Grid resolution	12 km $\times$ 12 km	12 km $\times$ 12 km	12 km $\times$ 12 km	$0.1^{\circ} \times 0.1^{\circ}$	$0.1^{\circ} \times 0.1^{\circ}$
inner domain
Grid resolution	36 km $\times$ 36 km	36 km $\times$ 36 km	36 km $\times$ 36 km	none	$0.5^{\circ} \times 0.25^{\circ}$
outer domain
Land-based emissions	CAMS-REG	CAMS-REG	CAMS-REG	CAMS-REG	CAMS-REG
Shipping emissions	STEAM	STEAM	STEAM	STEAM	STEAM
Biogenic emissions	MEGAN	MEGAN	MEGAN	Calculated online	Calculated online
	Model v2.03	Model v2.04	Model v3
Sea salt emissions	Calculation based	Calculation based	Calculation based	Calculation based	Calculation based
	on Ovadnevaite et	on Monahan et	on Kelly et	on Monahan et	on Monahan et
	al. (2014)	al. (1986)	al. (2010)	al. (1986) and	al. (1986) and
				Mårtensson et	Mårtensson et
				al. (2003)	al. (2003)
Dust emissions	Based on approach	Calculated online	Not considered	Key parameter is	Calculated online
	used in global EMAC			wind friction
	(ECHAM/MESSy;			velocity
	Klingmüller et
	al., 2018; Astitha
	et al., 2012)
Meteorological driver	WPS/WRF	WPS/WRF	COSMO-5 CLM	ECMWF (IFS)	ECWMF (IFS)
Boundary conditions	MOZART-4 output	Gaseous species:	IFS_CAMS	Boundary conditions	CAMS C-IFS
		LMDz-INCA model;	cycle45r1	provided with the
		aerosols:		open-source model
		GOCART model		distribution for
				year 2015
Chemical mechanism	CB05	MELCHIOR2	CB05	EmChem 19a	CBM-IV
Dry-deposition scheme	Resistance model of	Dry deposition	Dry deposition	As described in	Resistance approach
	Zhang et al. (2003)	is as in	scheme M3Dry	Simpson et	following Erisman
		Wesely (1989)	(Pleim et al., 2001)	al. (2012)	et al. (1994)

The model simulation runs should all contain NO $_{2}$ and O $_{3}$ in grams per cubic meter at an hourly resolution on a 2-D grid from the lowest layer and be provided as a netcdf file following CF conventions. The lowest layer on the ground was used in the present study.

With all CTMs, a reference run for the current air quality situation was performed, including all emissions (base case). Furthermore, all models did one run without the emissions from shipping (no-ship case). The difference between the calculations with all emissions and the calculation without shipping emissions is used to determine the potential impacts of ships on the ambient pollutant concentration. This method shows the change in an emission reduction and the maximal effect, by having a complete switch-off from shipping activity in the no-ship run. Thus, it is referred to as the zero-out method. This was done for all five models.

2.1.1 Model description CAMx

CAMx (Comprehensive Air Quality Model with Extensions) is an Eulerian photochemical dispersion model developed by Ramboll Environ. Version CAMx v6.50 of the model was used in the present study.

For this study, a first domain with a 36 km resolution was defined at the European scale. A second nested domain was defined and named MEDI12 ( $147 \times 249$ points), which covered the center of Europe with a resolution of 12 km. Both meteorological and chemical transport simulations were provided for these domains. WRFv3.9 was run for the simulation of meteorological conditions with 28 vertical layers up to 50 hPa, with FNL data for initial conditions.

For the CAMx simulation, boundary conditions from MOZART-4 were used.

Sea salt emissions are calculated in the SEASALT pre-processor of CAMx. This program generates aerosol emissions of sodium, sulfate and chloride and gaseous emissions of chlorine using CAMx-ready meteorological and land use files. The sea salt emissions program calculates the flux of sea salt over the open ocean using parameterizations developed by Ovadnevaite et al. (2014). The surf zone aerosol flux is calculated by using the Gong (2003) open-ocean approach with an assumed 100 % whitecap coverage. Biogenic emissions were calculated separately with MEGANv2.03 (Model of Emissions of Gases and Aerosols from Nature; Guenther et al., 2006) and then included in the land-based emissions. WBDUST pre-processors deliver dust emissions in CAMx and generate gridded windblown dust emissions. The scheme is based on an updated approach used in the global EMAC (ECHAM/MESSy) atmospheric chemistry–climate model (Klingmüller et al., 2018; Astitha et al., 2012). The mechanism for lightning NO $_{x}$ was not activated in CAMx.

The gas phase chemical mechanism is Carbon Bond 5 (CB05), in which the NMVOC emissions are split into 13 species (TERP, ISOP, XYL, TOL, ETOH, MEOH, IOLE, OLE, ETH, ALD2, PAR, ETHA and FORM) and describe approximately 156 reactions. For semivolatile inorganic species (sulfate, nitrate and ammonium), the equilibrium concentration is calculated using the thermodynamic model ISORROPIA (Nenes et al., 1998). Fourteen vertical levels are simulated with a first layer height of approximately 10 m.

2.1.2 Model description CHIMERE

CHIMERE is an offline chemistry transport model developed by LMD-IPSL/CNRS (Menut et al., 2013). The CHIMERE2017r4 version of the model was used in this study.

WRFv3.9 (Weather Research and Forecasting Model) was run for the simulation of meteorological conditions with 28 vertical layers up to 50 hPa, with FNL data for initial conditions.

Concerning the CHIMERE simulation, boundary conditions are monthly mean climatologies taken from the LMDz-INCA model (Laboratoire de Météorologie Dynamique General Circulation Model – INteraction with Chemistry and Aerosols; Schultz et al., 2006) for gaseous species and from the GOCART model (Global zone Chemistry Aerosol Radiation and Transport; Ginoux et al., 2001) for aerosols (desert dust, carbonaceous species and sulfate). Sea salt emissions were calculated as described in Monahan et al. (1986). MEGANv2.04 calculated biogenic emissions (Guenther et al., 2006). MEGAN is run directly by CHIMERE code, and biogenic emissions are just generated before the air quality run. The mineral dust emissions are calculated on-line. The soil is represented by relative percentages of sand, silt and clay with the USGS soil texture (https://www.usgs.gov/, last access: 20 January 2023). The aeolian roughness length used in CHIMERE is the GARLAP (Global Aeolian Roughness Lengths from ASCAT and PARASOL) dataset as in Prigent et al. (2012). There is no treatment of NO $_{x}$ lightning in CHIMERE.

The gas phase chemical mechanism is MELCHIOR2 (Modele Lagrangien de Chimie de l'Ozone a l'echelle Regionale), in which the NMVOC emissions are split into 10 species (C $_{2}$ H $_{6}$ , NC $_{4}$ H $_{10}$ , C $_{2}$ H $_{4}$ , C $_{3}$ H $_{6}$ , C $_{5}$ H $_{8}$ , OXYL, HCHO, CH $_{3}$ CHO, CH $_{3}$ COE and APINEN) and describe approximately 120 reactions. For semivolatile inorganic species (sulfate, nitrate and ammonium), the equilibrium concentration is calculated using the thermodynamic model ISORROPIA (Nenes et al., 1998). Nine vertical levels are selected with a first layer height at 20 to 25 m.

2.1.3 Model description CMAQ

On the basis of emission input data, the CMAQ Model v5.2 with the AERO6 model calculates air concentration as well as deposition fluxes of atmospheric gases and aerosols (Byun and Schere, 2006; Appel et al., 2017). Atmospheric chemistry is used by the chemical Carbon Bond 5 (CB05) mechanism (Sarwar et al., 2008) cb05tucl with updated toluene chemistry (Whitten et al., 2010), including the chlorine chemistry extension (CB05-TUCL; https://www.airqualitymodeling.org/index.php/CMAQv5.0_Chemistry_ Notes, last access: 20 January 2023). The aerosol scheme AERO6 is used for the formation of secondary inorganic aerosols. Sulfuric acid (H $_{2}$ SO $_{4}$ ), nitric acid (HNO $_{3}$ ), hydrochloric acid (HCl) and ammonia (NH $_{3}$ ) gas-phase–aerosol partition equilibria are solved by the ISORROPIA mechanism (Fountoukis and Nenes, 2007; Nenes et al., 1998). Contained within is the formation of secondary organic aerosol (SOA) from isoprene, terpenes, benzene, toluene, xylene and alkanes (Carlton et al., 2010; Pye and Pouliot, 2012).

Sea salt emissions were calculated as described in Kelly et al. (2010). Biogenic emissions (NMVOC from vegetation and soil NO) were calculated previously with MEGANv3 (Guenther et al., 2012) and then included in the land-based emissions. Emissions of windblown dust were not considered. CMAQ models 30 vertical layers, with the lowest layer from 0 to 42 m and the second layer from 42 to 85 m. The NO $_{x}$ lightning treatment in CMAQ was not activated for the present study.

The COSMO model simulated the meteorological data for CMAQ, applying the version COSMO5-CLM16 (Schultze and Rockel, 2018; Petrik et al., 2021). The MCIP (Meteorology-Chemistry Interface Processor) processed meteorological model output into the input format required for CMAQ. The vertical resolution of the meteorological model was 40 terrain-following geometric height levels up to 22 km. The boundary condition driver used was IFS-CAMS cycle45r1 (Integrated Forecasting System – Copernicus Atmosphere Monitoring Service; Inness et al., 2019) with a vertical resolution of 60 sigma levels up to 65 km.

To prevent the effects from initial conditions on the simulated atmospheric concentrations in 2015, the model run started with a spinup run in mid-December 2014. The grid size of the Mediterranean Sea domain was 12 km $\times$ 12 km, nested in a 36 km $\times$ 36 km domain covering all of Europe.

2.1.4 Model description EMEP

The EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesizing Centre – West, https://www.emep.int/mscw/index.html, last access: 20 January 2023) model is a limited-area, terrain-following hybrid coordinate model designed to calculate air concentrations and deposition fields for major acidifying and eutrophying pollutants, photooxidants and particulate matter (Simpson et al., 2012, 2020).

In this study, a $0.1^{\circ} \times 0.1^{\circ}$ resolution grid on a long–lat projection and with 20 vertical levels was used. The meteorological input data are based on forecast experiment runs with the Integrated Forecast System (IFS), a global operational forecasting model from the European Centre for Medium-Range Weather Forecasts (ECMWF). The meteorological fields are retrieved on $0.1^{\circ} \times 0.1^{\circ}$ long–lat coordinates. Vertically, the fields on 60 eta ( $η$ ) levels from the IFS model are interpolated onto the 20 EMEP eta levels.

The model version used was rv4.34 with chemical mechanism EmChem 19a (Simpson et al., 2012, 2020). The mechanism builds on surrogate VOC species (Simpson et al., 2012; extended with benzene and toluene) and has 171 gas phase and heterogeneous reactions. The model always assumes equilibrium between the gas and aerosol phases using the MARS equilibrium module (Model for an Aerosol Reacting System) of Binkowski and Shankar (1995). For secondary organic aerosol (SOA), a so-called volatility basis set (VBS) approach (Robinson et al., 2007; Donahue et al., 2009; Bergström et al., 2012) is used. All primary organic aerosol (POA) emissions are treated as nonvolatile to keep emission totals of both PM and VOC components the same as in the official emission inventories, while the semivolatile ASOA and BSOA species are assumed to oxidize (age) in the atmosphere by OH reactions (Simpson et al., 2012).

The generation of sea salt aerosol over oceans is driven by the surface wind, and the EMEP model's parameterization scheme for calculating sea salt generation is based on two source functions: those of Monahan et al. (1986) and Mårtensson et al. (2003). The following natural emissions are calculated in the model for each grid cell and at every model time step: biogenic emissions of isoprene and monoterpenes use near-surface air temperature and photosynthetically active radiation. Soil NO emissions from soils of seminatural ecosystems are specified as a function of N deposition and temperature. The key parameter driving dust emissions is wind friction velocity. Additionally, daily emissions from forest and vegetation fires are taken from the “Fire INventory from NCAR version 1.0” (FINNv1; Wiedinmyer et al., 2011). Emissions of NO $_{x}$ from lightning are included as monthly averages of global 3-D fields on a T21 ( $5.65^{\circ} \times 5.65^{\circ}$ ) resolution (Köhler et al., 1997). For this study, the initial and boundary conditions provided with the open-source model distribution for 2015 were used.

2.1.5 Model description LOTOS-EUROS

LOTOS-EUROS is an Eulerian chemistry transport model (Manders et al., 2017). The model simulates air pollution in the lower troposphere and is of intermediate complexity, allowing ensemble-based simulations and assimilation studies. LOTOS-EUROS performs hourly model output using ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological data. The gas phase chemistry follows the TNO CBM-IV scheme (Schaap et al., 2008).

For sea salt two parametrizations are used for online calculation of emissions: Mårtensson et al. (2003) for fine particles and Monahan et al. (1986) for coarse particles. Biogenic emissions are calculated online during the CTM run. For isoprene, a tree-species-dependent emission factor was used (Schaap et al., 2008; Beltman et al., 2013). NO emissions from soil were calculated as in Novak and Pierce (1993). Dust emissions are also calculated online for three sources of dust. Desert dust follows Mokhtari et al. (2012), and road resuspension and dust from agricultural processes follow a module developed by Schaap et al. (2008). There is no treatment of NO $_{x}$ lightning in LOTOS-EUROS.

LOTOS-EUROS has a dynamical vertical layer structure with five layers in total. The first layer is at 25 m, while the second layer follows the meteorological boundary layer. On top of that, two evenly distributed reservoir layers are defined: one up to 3500 m and one top layer up to 5000 m above sea level. The model has participated in multiple model intercomparison studies (Bessagnet et al., 2016; Colette et al., 2017), showing overall good performance.

2.2 Model domains and nesting

The domain for the intercomparison of a section of the Mediterranean Sea covered a spatial extent from a longitude of $- 0.95$ to 29.95 $^{\circ}$ and a latitude of 33.8 to 44.95 $^{\circ}$ . The grid cell size used was 12 km $\times$ 12 km interpolated on a $0.1^{\circ} \times 0.1^{\circ}$ grid nested in a larger 36 km $\times$ 36 km grid (except EMEP) covering all of Europe, as shown in Fig. 1. Computational domains of the CTMs can be found in Table S1 in the Supplement.

Figure 1

Domains and measurement stations. Red trapeze displays the 12 km $\times$ 12 km domain; black triangles are locations of measurement stations. At the bottom left, the larger 36 km $\times$ 36 km domain is displayed.

[Figure omitted. See PDF]

2.3 Emissions

2.3.1 Land-based emissions

Annual anthropogenic land-based gridded emissions for 2015 obtained from the CAMS-REG v2.2 emission inventory were used as input by all five compared models. Gridded emission files contain GNFR (Gridded Nomenclature for Reporting) emission sectors for each country for the air pollutants NO $_{x}$ , SO $_{2}$ , NMVOC, NH $_{3}$ , CO, PM $_{10}$ , PM $_{2.5}$ and CH $_{4}$ . The emissions are provided at a spatial resolution of $1 / 10^{\circ} \times 1 / 20^{\circ}$ in longitude and latitude (i.e., $\sim 6$ km $\times$ 6 km over central Europe).

The height distribution of emissions per GNFR sector was determined as described in Bieser et al. (2011b). The temporal distribution was determined by separating the annual emissions of each sector into hourly emission data with data splitting as described in Granier et al. (2019). PM was split as described in Bieser et al. (2011a); NO $_{x}$ was split according to Manders-Groot et al. (2016). NMVOC emissions were given for different sectors, using the GNFR, and were separated country-wise. This split was used as provided in the CAMS-REG v2.2 emission inventory (Granier et al., 2019). The species were afterwards split within each CTM according to their chemical mechanism. Information on biogenic emission totals for the whole model domain can be found in Table S20.

2.3.2 Shipping emissions

The shipping emission dataset produced with the STEAM model has a spatial resolution of 12 km $\times$ 12 km and a temporal resolution of 1 h. The STEAM emissions are divided into two vertical layers (0 to 36 m; 36 to 1000 m) and are provided for mineral ash, carbon monoxide (CO), carbon dioxide (CO $_{2}$ ), elemental carbon (EC), NO $_{x}$ , organic carbon (OC), PM $_{2.5}$ , particle number count (PNC), sulfate (SO $_{4}$ ), SO $_{x}$ (containing SO $_{2}$ and SO $_{3}$ ) and VOC. To reduce the number of generated emission maps and the computational resources needed to run the STEAM model, VOC emissions were divided into four categories based on how their emission factors change as a function of the engine load. Emissions of individual VOC species were calculated afterwards based on their mass fractions of the total emissions in the VOC group. Emission factors for VOC are based on the average values taken from various publications (Agrawal et al., 2008, 2010; Sippula et al., 2014; Reichle et al., 2015).

In CAMx, all shipping emissions are put in the first layer. For CHIMERE, all shipping emissions above 36 m and 88 % of the emissions below 36 m have been added to the second layer. Only 12 % of the emissions below 36 m were emitted in the first layer of the model. This was calculated based on the STEAM emission dataset and the stack heights contained therein. Additionally, in CMAQ, shipping emissions were distributed in the two lowest layers, emissions below 36 m were attributed to the lowest layer, and emissions above 36 m were in the second layer. For EMEP simulations, the STEAM emissions were summed from hourly to daily emissions and attributed to the lowest layer (up to 90 m). In LOTOS-EUROS, $\sim 70$ % of emissions below 36 m are assigned to the first layer, which is 25 m thick, and $\sim 30$ % to the second layer. Emissions above 36 m are divided over different height classes: 30 % between 36 and 90 m, 30 % between 90 and 170 m, 30 % between 170 to 310 m, and 10 % between 310 cm and 470 m. Due to the dynamic second model layer (following the meteorological boundary layer), those emissions are put in the second and/or third model layer. In the case of a well-mixed and vertically extended meteorological boundary layer (above 470 m), all emissions are in this second layer, whereas when the boundary layer is shallow, some emissions are put in the third layer.

2.4 Deposition mechanisms

Deposition velocities for gaseous species in CHIMERE, CMAQ and LOTOS-EUROS are based on the formula introduced by Wesely (1989). This formula is the reciprocal sum of aerodynamic resistance ( $R_{a}$ ), quasi-laminar sublayer resistance ( $R_{b}$ ) and surface resistance ( $R_{c}$ ). Nevertheless, all models differ in calculating the single variables. $R_{a}$ depends on meteorology and surface roughness, which is model dependent. $R_{b}$ is determined by the friction velocity, depending on the surface type. $R_{c}$ is the bulk surface resistance, containing different components, i.e., leaf stomata, soil, leaf litter, etc. All of these components use input data that are unique for each model.

In CHIMERE, $R_{b}$ is estimated following Hicks et al. (1987). The resistance $R_{c}$ formulation follows Erisman et al. (1994) and the developments made in the EMEP model (Emberson et al., 2000; Simpson et al., 2003, 2012). It uses a variety of additional resistances, mostly to account for stomatal and surface processes, both of which depend on the land use type and season. In CMAQ, the m3dry mechanism was used, which takes $R_{a}$ and $R_{b}$ from the provided meteorological data. $R_{c}$ is calculated in CMAQ as described in Pleim and Ran (2011).

In EMEP quasi-laminar layer resistance $R_{b}$ follows Hicks et al. (1987). Surface (or canopy) resistance, $R_{c}$ , is the most complex variable in the deposition model, the calculation of which is described in Simpson et al. (2012). The resistance $R_{b}$ in LOTOS-EUROS is described following the EDACS system (Erisman et al., 1994). In van Zanten et al. (2010), the parametrizations of different resistances $R_{c}$ that contribute to resistance for dry deposition of NO $_{2}$ and O $_{3}$ are described, depending on land use type. The Deposition of Acidifying Compounds (DEPAC) 3.11 module was used in LOTOS-EUROS, following the resistance approach (van Zanten et al., 2010; Wichink Kruit et al., 2012).

CAMx uses the gas resistance model of Zhang et al. (2003), which is very similar to the Wesely formulations with regard to $R_{a}$ and $R_{b}$ . However, the $R_{c}$ is expressed as several more serial and parallel resistances, based on Wesely (1989), but with some adjustments within CAMx (Ramboll Environment and Health, 2020).

2.5 Observational data, statistical analysis and model results

Model results for total surface concentrations of NO $_{2}$ and O $_{3}$ from the five CTMs are evaluated against available measurements of the air quality monitoring network taken from the download service of air quality of the European Environment Agency EEA (https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm, last access: 20 January 2023). NO $_{2}$ concentrations are monitored at 62 and O $_{3}$ at 48 background stations. Figure 1 shows the locations of the measurement stations and detailed information on the stations is given in Appendix B.

The criteria for the selection of the stations were as follows: (i) station type is “background”, (ii) elevation is below 1000 m, and (iii) data for more than one of the pollutants NO $_{2}$ , O $_{3}$ or PM $_{2.5}$ are available. The PM $_{2.5}$ measurements were chosen for further evaluation in this intercomparison project. Preferably, stations close to the sea were chosen since simulating potential ship impacts was the major focus of this study. There was no exact threshold for the distance to the coastline assumed, but preferably stations at a distance $< 30$ km from the coast were chosen. Some stations further inland were chosen to check the model performance. Furthermore, the domain was divided into four parts (“west”, “north”, “south”, “east”), and a roughly equal number of stations should be in each parcel (map in Fig. S2). The measured concentrations at the stations were compared to the results of simulations of the CTMs. For this purpose, the grid cell of the respective monitoring station was determined, and modeled concentrations were taken from there.

To quantify the CTMs performance, the root mean square error in the modeled values (RMSE), normalized mean bias (NMB) and Spearman's correlation coefficient ( $R$ ) were calculated for each monitoring station, as described in Appendix A. A categorization for correlation was performed as described in Schober et al. (2018), adjusted and displayed in Table 2.

Table 2

Interpretation of the correlation coefficient ( $R$ ), as described in Schober et al. (2018) (adjusted).

Magnitude of $R$	Interpretation
0.00–0.39	Weak correlation
0.40–0.69	Moderate correlation
0.70–1.00	Strong correlation

Time series were used to compare the modeled daily mean concentrations to observations at exemplary stations. In addition, the annual mean ship impact was calculated based on hourly data. For a graphical comparison of the model performances $R$ , NMB and RMSE, boxplots were used based on annual values calculated from hourly data at each station. For the intercomparison spatial distribution, annual mean values based on the hourly data are used. The correlation $R$ between models was calculated for each grid cell based on hourly data.

3 Results and discussion

In the following section, the results for NO $_{2}$ and O $_{3}$ model performance and spatial distribution will be shown. Afterward, O $_{x}$ and NO $_{x}$ will be displayed for a more detailed investigation of the photochemistry and lifetime of the species. The results of dry deposition of NO $_{2}$ and O $_{3}$ will be considered in Sect. 3.4.

3.1 Model performance and intercomparison

To evaluate the performance of the CTMs, simulated concentrations considering all emission sectors (base case) for annual values of 2015 were compared to actual measured data of NO $_{2}$ and O $_{3}$ . Based on the results of the five models for the cases with (base case) and without shipping emissions (no-ship case), potential impacts of the shipping sector on the NO $_{2}$ and O $_{3}$ concentrations were estimated. Figures of spatial distribution display the annual mean values for 2015 and the potential relative ship impacts. With this setup, the model performance and potential ship impact of the different models can be directly compared.

3.1.1

NO $_{2}$ model performance

Table 2 contains $R$ , NMB and RMSE based on the annual time series for NO $_{2}$ at all stations. The highest correlation across all 62 stations showed LOTOS-EUROS followed by CMAQ with a slightly lower correlation (LOTOS-EUROS: $R = 0.45$ ; CMAQ: $R = 0.42$ ), whereas for CHIMERE, EMEP and CAMx, non-existent to weak correlation was found ( $R = 0.08$ to $R = 0.10$ ). The NMB suggests that all five CTMs underestimate the annual mean concentrations at most measurement sites; the NMB for all stations is negative for all models. The RMSE is within the same range for all models (RMSE $= 15.6$ to 19.5 $µ g m^{- 3}$ ; Table 3).

Table 3

Correlation, normalized mean bias (NMB), root mean square error (RMSE), and observational (obs) and simulated (sim) mean values of NO $_{2}$ for 2015: first data were averaged station-wise and then averaged for all 62 stations.

	Correlation $R$	NMB	RMSE	Sim	Obs
			( $µ g m^{- 3}$ )	( $µ g m^{- 3}$ )	( $µ g m^{- 3}$ )
CAMx	0.08	$- 0.32$	19.5	8.1
CHIMERE	0.10	$- 0.52$	18.5	5.8
CMAQ	0.42	$- 0.56$	17.3	6.7	16.6
EMEP	0.10	$- 0.40$	18.8	7.1
LOTOS-EUROS	0.45	$- 0.52$	15.6	7.6

Time series for three example stations show the temporal variations between measured and modeled data (Appendix C). The supplements provide an overview of the mean values of stations in each map parcel (west, north, south, east; Fig. S2). Figure C1 displays a time series at an urban background station in France (fr08614, “Gauzy”; lat 43.8344, long 4.374219), which was chosen because southern France will be investigated in greater detail as part of this study. Figure C2 shows a rural background station in Italy (it1773a, “Genga – Parco Gola della Rossa”; lat 43.46806, long 12.95222), which was chosen due to its central location in the domain and the high number of stations in Italy. Figure C3 displays the time series at a station in Greece (gr0035a, “Lykovrysi”; lat 38.06963, long 23.77689) to include a station in the eastern part of the domain.

Measurements at the French station show the highest NO $_{2}$ values in winter, with peaks between 40 and 55 $µ g m^{- 3}$ (Fig. C1). LOTOS-EUROS and EMEP underestimate the values throughout the year. Moderate correlation was calculated for CMAQ ( $R = 0.6$ ) and LOTOS-EUROS ( $R = 0.65$ ) at this station. The simulated ship impact has annual mean values from 0.2 $µ g m^{- 3}$ (EMEP, CAMx) to 0.6 $µ g m^{- 3}$ (CMAQ) at station fr08614. Shipping emissions have a potential relative impact between 1.8 % (EMEP) and 6.7 % (CMAQ) on the total concentration in the annual mean. The highest potential ship impact at this station was modeled by CMAQ. At the Italian station, it1773a lower NO $_{2}$ concentrations were measured compared to the station in France. The highest peaks are approximately 20 $µ g m^{- 3}$ in winter. At station it1773a, the potential ship impact on the total NO $_{2}$ concentration has annual mean values between 0.07 $µ g m^{- 3}$ (LOTOS-EUROS) and 0.5 $µ g m^{- 3}$ (CAMx). The highest relative potential ship impact was 7.9 % and was modeled by CAMx. At station gr0035a, the lowest simulated values are shown by CMAQ and LOTOS-EUROS. The highest values display EMEP at this station, also with the highest correlation between measured and simulated data ( $R = 0.55$ ). The potential ship impact at the Greek station is between 5.0 % (EMEP) and 15.3 % (CAMx), which is higher than the potential ship impact at the other two stations.

All CTMs underestimate the actual measured total NO $_{2}$ values at both stations, except for LOTOS-EUROS in Italy. None of the models is able to model matching peak values. Neither at the station in France, Italy nor Greece did models show seasonal variation in concentrations, whereas NO $_{2}$ usually has higher values in winter and lower values in summer, mainly because of lower photolytical degradation and suppressed vertical mixing, as described, e.g., in Ordóñez (2006).

Differences in potential ship impacts between the stations are caused by the location and station type (fr08614: urban background; it1773a: rural background; gr0035a: suburban background). At the French station, the traffic-related NO $_{2}$ concentration might supersede the ship-related NO $_{2}$ . The station in Italy is not located in a city, so the NO $_{2}$ concentration caused by ships comes to the fore. The highest potential ship impact was simulated at the station in Greece because it is suburban but close to the Port of Piraeus, which is one of the largest ports in the Mediterranean Sea. As expected, the average potential ship impact is low at stations that are not directly located on the coast or near a harbor.

To compare the correlation $R$ , NMB and RMSE at all measurement stations for all models, the results of the comparison are divided by country and displayed in boxplots (Fig. 2). Each dot displays one measurement station. The correlation measured against the simulated annual mean NO $_{2}$ is highest for LOTOS-EUROS and CMAQ in all countries, reflecting the results shown in Table 3 for correlation. Nevertheless, boxplots for NMB and, in particular, for RMSE show that differences among countries are larger than differences among the models (Fig. 2b, c). This means that all models show good or bad performance at some stations, which was not found to be statistically relevant.

Figure 2

(a) Correlation, (b) NMB and (c) RMSE for annual mean NO $_{2}$ concentration based on hourly data. Dots display annual mean values at measurement stations for the respective countries (al: Albania; es: Spain; fr: France; gr: Greece; hr: Croatia; it: Italy; me: Montenegro; tr: Turkey). Boxplots are for the models with the boxes displaying the interquantile range (IQR) between the 25th (Q1) and 75th (Q3) percentile, the black line displays the median (Q2), and whiskers are calculated as Q1 $- 1.5 \times IQR$ (minimum) and Q3 $+ 1.5 \times IQR$ (maximum).

[Figure omitted. See PDF]

Underestimations by models of NO $_{2}$ at urban sites were found in other studies (Karl et al., 2019b; Giordano et al., 2015), despite differences in grid size. Karl et al. (2019b) used a grid resolution of 4 km, and Giordano et al. (2015) used a grid resolution of $\sim 0.25$ $^{\circ}$ (27 to 28 km). The underestimation might be due to too low emissions in the inventory used by the models and the heterogeneity of emissions. Regional CTMs cannot display small-scale spatial heterogeneity; coarse grid cells are not representative of the measurement location. Giordano et al. (2015) suggested in their study that the underestimation of NO $_{2}$ could be caused by either an underestimation of the chemical lifetime of NO $_{x}$ , excessively high dry deposition, an underestimation of natural emissions at rural and remote stations, or a combination of these factors. Differences in radical concentrations and reactive nitrogen might be additional reasons for underestimation (Knote et al., 2015).

The model performance of NO $_{2}$ has shown that differences in time series between the models occur, caused by the differences in meteorology and large grid size. Large grid sizes can cause errors insofar as, in simulations, the land areas are not seen as such but as water areas. This is especially problematic when having measurement stations located close to the sea.

3.1.2

NO $_{2}$ spatial distribution

The simulated annual mean NO $_{2}$ concentrations considering all emission sectors are similar for all CTMs, with a median ensemble mean of $\sim 2.5$ $µ g m^{- 3}$ (Fig. 3a). Regarding the spatial distribution CAMx and CHIMERE have the largest areas, with values exceeding 5.0 $µ g m^{- 3}$ , especially along the main shipping routes and in urban areas (Fig. 4). The CMAQ, EMEP and LOTOS-EUROS figures look similar, which is in good agreement with the displayed time series in Sect. 3.1, where the results are within the same range.

Figure 3

Annual mean for all grid cells in the whole model domain. (a) Mean NO $_{2}$ for all emission sectors (base case); (b) mean NO $_{2}$ for shipping only; (c) relative potential ship impact on total NO $_{2}$ concentration. All_mean is the mean value of all models, with a median of (a) 2.8 $µ g m^{- 3}$ , (b) 0.7 $µ g m^{- 3}$ and (c) 27.7 $µ g m^{- 3}$ .

[Figure omitted. See PDF]

Figure 4

Annual mean NO $_{2}$ total concentration. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean NO $_{2}$ concentration, referring to the whole model domain.

[Figure omitted. See PDF]

Over land area, all model simulations display a concentration pattern ranging within 1 order of magnitude. Nevertheless, the frequency distributions of the CMAQ, EMEP and LOTOS-EUROS simulations show the highest frequency between 1.0 and 2.0 $µ g m^{- 3}$ , whereas for CAMx and CHIMERE, they are more equally distributed. Higher values of NO $_{2}$ concentrations simulated by CAMx and CHIMERE might indicate a longer lifetime of NO $_{2}$ in the atmosphere. NO $_{2}$ reacts quickly with hydroxyl radicals (OH) and forms HNO $_{3}$ , or NO $_{2}$ photolysis creates O $_{3}$ during the daytime. The annual mean HNO $_{3}$ concentrations are between 2.0 to 5.0 $µ g m^{- 3}$ for CAMx and CHIMERE over water areas and are 0.8 to 2.0 $µ g m^{- 3}$ over water areas for CMAQ, EMEP and LOTOS-EUROS (Fig. S11). Over land areas, the HNO $_{3}$ concentrations are within one range for all models. A lower HNO $_{3}$ concentration is expected for CTMs with a longer lifetime of atmospheric NO $_{2}$ . Nevertheless, there can be a misinterpretation when both concentrations are high. Therefore the data were normalized by using the ${HNO}_{3} : {NO}_{2}$ ratio (Fig. S12). Differences are displayed especially along the main shipping routes. There, values are lower in CAMx and EMEP compared to the other models. This can be explained by the lower HNO $_{3}$ formation by these models along the shipping routes.

Also the meteorology might influence the vertical mixing of NO $_{2}$ . This leads to differences between the models or explains the similarity between CAMx and CHIMERE due to the use of the same meteorology. Nevertheless, this point will not be discussed here in detail since in the present study only the lowest layer was considered and the vertical mixing processes were not evaluated.

The correlation between the models for total NO $_{2}$ concentration was calculated based on hourly data (Table 4). The highest correlation was found between CAMx and CHIMERE ( $R = 0.80$ ). Weak correlations were found between LOTOS-EUROS and CAMx ( $R = 0.31$ ) and LOTOS-EUROS and CHIMERE ( $R = 0.36$ ). This weak correlation is due to the differences in frequency distribution, with LOTOS-EUROS showing most values below 1.0 $µ g m^{- 3}$ , whereas for CAMx and CHIMERE, more values are located in the higher value ranges. Overall, the models can give a robust estimate regarding the base run of the annual mean of NO $_{2}$ .

Table 4

Correlation for the NO $_{2}$ base run between models for the whole domain (all grid cells), based on hourly data for NO $_{2}$ total concentration.

All	CAMx	CHIMERE	CMAQ	EMEP	LOTOS-EUROS
LOTOS-EUROS	0.31	0.36	0.71	0.73	–
EMEP	0.39	0.44	0.73	–
CMAQ	0.39	0.43	–
CHIMERE	0.80	–
CAMx	–

The highest potential impact of ships on total NO $_{2}$ concentrations was found on the main shipping routes, with values $> 85$ % (Fig. 5). Similar values were found for the Baltic Sea (Karl et al., 2019b) and for the Iberian Peninsula (Nunes et al., 2020). CHIMERE and CAMx model the highest values over the sea region, with a potential ship impact on NO $_{2}$ between 60 % and 85 %. CMAQ, LOTOS-EUROS and EMEP have similar patterns for ship impacts over the sea.

Figure 5

Annual mean NO $_{2}$ potential ship impact. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean NO $_{2}$ potential ship impact, referring to the whole model domain.

[Figure omitted. See PDF]

On the Mediterranean coastline, CMAQ, CHIMERE, LOTOS-EUROS and EMEP simulate a similar potential impact, with 25 % to 45 % potential ship impacts on total NO $_{2}$ . Merico et al. (2017) found similar results in a study with an NO $_{2}$ shipping impact of up to 32.5 % regarding four port cities in the Adriatic–Ionian Sea. CAMx reveals a higher impact with $> 85$ % at the coastline. The potential ship impact displayed in the time series in Sect. 3.1 was lower, although the measurement stations were not far from the coast. This shows that although the potential impact from ships reaches regions far from the coast, the highest impact is over the sea area. The frequency distribution for the relative ship impact shows that all models simulate the most values between 0 % and 5.0 % of the potential ship impact. Interestingly, the distribution is lowest at values between 20 % and 40 % (CMAQ, EMEP, LOTOS-EUROS) and 60 % (CAMx, CHIMERE) and then increases again at higher values, showing a bimodal distribution. This is due to large areas with high potential impacts over water and large areas with low potential impacts over land or near harbors.

Over land in the northeast area of the domain, slightly negative potential ship impacts are derived from the CMAQ, CAMx, LOTOS-EUROS and EMEP results. CHIMERE shows only very few negative values but in the same region. Negative potential ship impacts on NO $_{2}$ concentrations may arise when the zero-out method is applied. They are a consequence of the nonlinear NO $_{x}$ gas phase chemistry. Especially in areas where the impact of NO $_{x}$ emissions from shipping is very low, less NO oxidation takes place because the additional NO from shipping in other areas has already consumed the oxidants (e.g., O $_{3}$ ).

The boxplots in Fig. 3 display the annual mean values for the whole model domain of NO $_{2}$ . Model results vary for the base run but also for the potential ship impact. This variability needs to be taken into account when the predictive power of CTMs is considered. The “all_mean” boxplot displays the mean of all models and shows that in comparison with other models, CAMx has high values. It further helps to show which CTM tends to simulate higher or lower values compared to others. The all_mean boxplots show similar ranges as boxplots for CMAQ and EMEP, particularly regarding absolute and relative potential ship impacts. Additionally, models simulating a higher overall concentration of pollutants also tend to simulate a higher potential ship impact. The relative potential ship impact is highest for CAMx and CHIMERE and lowest for LOTOS-EUROS.

3.1.3

O $_{3}$ model performance

The tropospheric O $_{3}$ concentrations are strongly connected to the NO $_{2}$ concentration and to the oxidized nitrogen chemistry in the atmosphere. O $_{3}$ can be both an initiator and a product of photochemistry; thus, it is crucial in tropospheric chemistry.

Simulated versus measured data of 1-year daily mean O $_{3}$ time series show a weak (EMEP: $R = 0.38$ ) to moderate correlation (CAMx: $R = 0.40$ ; CHIMERE: $R = 0.47$ ; CMAQ: $R = 0.60$ ; LOTOS-EUROS: $R = 0.69$ ; Table 5).

Table 5

Correlation, normalized mean bias (NMB), root mean square error (RMSE), and observational (obs) and simulated (sim) of O $_{3}$ as the mean values for 2015: the first data were averaged station-wise and then averaged for all 48 stations.

	Correlation $R$	NMB	RMSE	Sim	Obs
			( $µ g m^{- 3}$ )	( $µ g m^{- 3}$ )	( $µ g m^{- 3}$ )
CAMx	0.40	0.41	40.5	90.4
CHIMERE	0.47	0.57	45.4	100.7
CMAQ	0.60	0.28	31.2	82.2	66.5
EMEP	0.38	0.37	39.0	87.6
LOTOS-EUROS	0.69	0.36	32.6	87.7

Selected time series represent these differences in correlation (Appendix D). Nevertheless, for the first months of the year CHIMERE, CAMx and CMAQ overestimate the actual measured O $_{3}$ values (Fig. D1: station fr08614; Fig. D2: station it1773a; Fig. D3: gr0035a).

During summer months, O $_{3}$ shows the highest values due to increased photochemical activity. The simulated potential ship impact is between 1.1 $µ g m^{- 3}$ (CAMx) and 2.8 $µ g m^{- 3}$ (LOTOS-EUROS) at station fr08614 and has a relative potential impact between 1.3 % (CAMx) and 4.0 % (CHIMERE) on the total concentration. At station it1773a, the mean O $_{3}$ potential ship impact is between 1.0 $µ g m^{- 3}$ (CAMx) and 3.0 $µ g m^{- 3}$ (CHIMERE), and the relative potential impact ranges from 1.1 % (CAMx) to 3.5 % (LOTOS-EUROS). The potential ship impact of station gr0035s ranges from $- 0.1$ $µ g m^{- 3}$ (CAMx) to 3.7 $µ g m^{- 3}$ (CMAQ; LOTOS-EUROS), which is a relative potential impact of $- 0.1$ % (CAMx) and 3.7 % (CMAQ).

The O $_{3}$ potential ship impact is within the same range at both stations and for all five CTMs. Figure 6 shows that CMAQ has the smallest bias compared to the other models (NMB $= 0.28$ ), followed by LOTOS-EUROS (NMB $= 0.36$ ). The RMSE is lowest for CMAQ (RMSE $= 31.2$ $µ g m^{- 3}$ ) and LOTOS-EUROS (RMSE $= 32.6$ $µ g m^{- 3}$ ), along with the lower NMB compared to the other models. The performance analysis revealed that all five models predict higher O $_{3}$ concentrations than those measured at almost all stations (NMB $> 0$ ). The overestimation of actual measured O $_{3}$ by the models is in line with results from previous studies (Karl et al., 2019b; Appel et al., 2017; Im et al., 2015a, b). Im et al. (2015a) showed that O $_{3}$ concentrations above 140 $µ g m^{- 3}$ are underestimated, while concentrations below 50 $µ g m^{- 3}$ are overestimated by 40 % to 80 % in all considered models. This overestimation of O $_{3}$ by the models is likely linked to the chemical boundary conditions used in the regional CTMs. Analyses of the boundary conditions revealed that, especially in winter, O $_{3}$ levels are mostly driven by transport instead of local production due to limited photochemistry (Giordano et al., 2015).

Figure 6

(a) Correlation, (b) NMB and (c) RMSE for annual mean O $_{3}$ concentration. Dots display values at measurement stations for the respective countries (al: Albania; es: Spain; fr: France; gr: Greece; hr: Croatia; it: Italy; me: Montenegro; tr: Turkey). Boxplots are for the models with the boxes displaying the interquantile range (IQR) between the 25th (Q1) and 75th (Q3) percentile, the black line displays the median (Q2), and whiskers are calculated as Q1 $- 1.5 \times IQR$ (minimum) and Q3 $+ 1.5 \times IQR$ (maximum).

[Figure omitted. See PDF]

CHIMERE uses boundary conditions from monthly mean climatologies simulated with the LMDz-INCA model, CAMx uses MOZART-4 output, LOTOS-EUROS and CMAQ use IFS-CAMS reanalysis data, and the EMEP model uses ozone boundary conditions provided with the open-source model distribution for 2015. These differences in input for the boundary conditions can be seen as the reason for the varying results in O $_{3}$ (Figs. S13 to S16).

All CTMs performed relatively well and are able to represent the course of the year, with higher values in summer and lower values in winter. Nevertheless, in some cases, the values in spring are overestimated.

3.1.4

O $_{3}$ spatial distribution

The annual mean concentration of O $_{3}$ considering all emission sectors is between 60 and 120 $µ g m^{- 3}$ for all models (Fig. 8). This is consistent with the measurements displayed in the time series in Sect. 3.2.1. CHIMERE, CAMx and LOTOS-EUROS show particularly high O $_{3}$ concentrations over the sea. Interestingly, EMEP results are similarly high over the sea area, but in comparison with other CTMs, concentrations are lower over land, and even values below 60 $µ g m^{- 3}$ can be seen in the Po valley (Fig. 8d). Regarding the correlation between the models for total concentration over the whole domain, it is highest between CMAQ and EMEP ( $R = 0.71$ ) and lowest for CAMx and LOTOS-EUROS ( $R = 0.42$ ), but predominantly moderate correlations were found among the models (Table 6).

Table 6

Correlation between models for the whole domain (all grid cells) based on hourly data for O $_{3}$ total concentration.

All	CAMx	CHIMERE	CMAQ	EMEP	LOTOS-EUROS
LOTOS-EUROS	0.42	0.59	0.58	0.59	–
EMEP	0.44	0.58	0.71	–
CMAQ	0.50	0.56	–
CHIMERE	0.63	–
CAMx	–

In general, all CTMs show high annual mean concentrations over the sea areas and low annual mean concentrations over land areas. This is due to lower dry deposition over sea and the overall higher emissions over land. Furthermore, high values of O $_{3}$ are expected to enter the domain from the eastern part of the Mediterranean Sea. This point will be discussed in Sect. 4. The frequency distribution of the annual mean total concentration of O $_{3}$ has a bimodal distribution for CHIMERE, CMAQ and EMEP. This reflects photochemical O $_{3}$ depletion or production, with high values over water areas and lower values over land. Over water, low O $_{3}$ depletion is expected during the night. A comparison of diurnal cycles of O $_{3}$ over water and over land shows that this presumption is reflected by CMAQ and EMEP results, showing more pronounced cycles of O $_{3}$ in grid cells over land (Fig. S17). However, the diurnal cycles of CAMx, CHIMERE and LOTOS-EUROS do not show differences in amplitude over land and water. Despite this, over water, all models show a higher spread of values within diurnal cycles, displaying that there is more variability in the course of the year over water than over land.

The potential relative impact of ships on total O $_{3}$ concentrations is lowest in areas with a high potential impact of shipping on total NO $_{2}$ (Fig. 9). It decreases to $- 20$ % in areas with high NO $_{2}$ concentrations in all model results, displaying a local-scale titration of O $_{3}$ by NO, which is emitted by ships. This reverse relationship between NO $_{2}$ and O $_{3}$ was already shown in other studies (e.g., Karl et al., 2019a). Measurement studies also indicate that emissions of NO lead to local reduction in O $_{3}$ concentration and showed that there could be an increase at larger distances (Merico et al., 2016). Consequently, the largest areas with O $_{3}$ destruction for the CAMx and CHIMERE coincide with areas where the models show the highest potential impact of shipping on NO $_{2}$ . The comparison with the time series shows the highest potential ship impact on the total O $_{3}$ concentration in summer. Likewise, in Sect. 3.1.4 the lowest potential ship impact was found for CAMx.

Figure 7 shows boxplots with annual mean values of the models for the whole domain. It shows that CAMx, CHIMERE and LOTOS-EUROS are within one range regarding the annual mean total concentration. The CMAQ and EMEP simulations are lowest for the annual mean O $_{3}$ total concentration. Regarding potential ship impact, all CTMs except CAMx are within one range.

Figure 7

Annual mean for the whole model domain. (a) Mean O $_{3}$ for all emission sectors (base case); (b) mean O $_{3}$ for shipping only; (c) relative potential ship impact on total O $_{3}$ concentration. All_mean is the mean value of all models, with a median of (a) 92.4 $µ g m^{- 3}$ , (b) 4.0 $µ g m^{- 3}$ and (c) 4.2 $µ g m^{- 3}$ .

[Figure omitted. See PDF]

Figure 8

Annual mean O $_{3}$ total concentration. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS; the panels show the emisbase spatial distribution and the annual mean value, and white areas contain values below 60 $µ g m^{- 3}$ . Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{3}$ concentration, referring to the whole model domain.

[Figure omitted. See PDF]

Figure 9

Annual mean O $_{3}$ potential ship impact. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS; white areas in the panels display values below $- 20$ %. Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{3}$ potential ship impact, referring to the whole model domain.

[Figure omitted. See PDF]

The present study does not contain the parts of the Mediterranean Sea furthest east due to the focus of the project on the western Mediterranean Sea with its harbor cities as well as due to the limited extent of the WRF domain. A more detailed investigation of the boundary conditions of CMAQ has shown high O $_{3}$ values in the eastern part of the domain. A high O $_{3}$ production over the eastern Mediterranean Sea and a steep west–east gradient of O $_{3}$ were described in previous studies (i.e., Doche et al., 2014; Safieddine et al., 2014; Liu et al., 2009). This production influences the amount of O $_{3}$ in the western part of the Mediterranean Sea. Safieddine et al. (2014) found an increase of up to 22 % in O $_{3}$ in the eastern part of the Mediterranean Basin compared to the middle of the basin. Doche et al. (2014) described a steep west–east O $_{3}$ gradient with the highest concentrations over the eastern part of the Mediterranean Basin.

Overall, all models showed a relatively good performance for O $_{3}$ but differed in simulating spatial distribution and potential ship impact mainly over water. Although boxplots for annual mean values of O $_{3}$ differ, for relative potential ship impact they show that CHIMERE, CMAQ, EMEP and LOTOS-EUROS are within one range. Diurnal cycles did not reveal differences in O $_{3}$ depletion over water and land among the models.

3.2

O $_{x}$ spatial distribution

The oxidation of VOCs produces O $_{3}$ in the troposphere when nitrogen oxides (NO; NO $_{2}$ ) and sunlight are present. Central to understanding this production is the photostationary state formed between NO, NO $_{2}$ and O $_{3}$ in sunlight. In emission-free air, a steady equilibrium would be expected; nevertheless, emission sources disturb this equilibrium. In areas with high NO emissions, O $_{3}$ destruction is expected, resulting in lower O $_{3}$ concentrations along the main shipping routes, in urban areas and in harbor cities.

The results show that all five CTMs tend to underestimate NO $_{2}$ and overestimate O $_{3}$ but at different magnitudes. For a better understanding of photochemical air pollution and chemical coupling, the oxidant levels ( $O_{x} = O_{3} + {NO}_{2}$ ) were calculated and displayed for all emission sources and for the potential ship impact. Clapp and Jenkin (2001) showed that the concentration of O $_{x}$ levels can be described as an NO $_{x}$ -independent regional impact, where the O $_{x}$ impact equates to the O $_{3}$ background, and an NO $_{x}$ -dependent local impact. The NO $_{x}$ -dependent impact correlates with the primary pollution, coming from direct NO $_{2}$ emissions or VOC, which promote conversion from NO to NO $_{2}$ (Clapp and Jenkin, 2001).

In comparison with the O $_{3}$ spatial distribution and frequency distribution, the annual mean concentration of O $_{x}$ displays a similar pattern between the results (Fig. 10). As was the case for O $_{3}$ , CHIMERE and CAMx show the highest values over the sea area, and EMEP shows the lowest values over land areas. The frequency distribution shows bimodally distributed values for CHIMERE, CMAQ and EMEP, as for O $_{3}$ . Thus, O $_{x}$ levels are mainly NO $_{x}$ -independent.

Figure 10

Annual mean O $_{x}$ ( $= {NO}_{2} + O_{3}$ ) concentration. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{x}$ concentration, referring to the whole model domain.

[Figure omitted. See PDF]

Nevertheless, NO $_{x}$ -dependent O $_{x}$ formation can also be seen in the potential ship impact on the total O $_{x}$ concentration (Fig. 11). The relative potential impact of O $_{x}$ displays how much substances from ships are added to the atmosphere. O $_{x}$ shows a strong conversion of NO $_{2}$ and O $_{3}$ ; thus the shipping lanes are no longer visible. High O $_{x}$ potential impacts over water areas for CHIMERE, CMAQ, EMEP and LOTOS-EUROS indicate the local potential impact from shipping emissions (NO $_{2}$ and VOC), which cause high O $_{x}$ levels in these areas. For CAMx, the O $_{x}$ potential impact was lower. This might be traced back to the overall higher concentration of NO $_{2}$ and O $_{3}$ in CAMx, leading to a lower proportion of other substances. Also, the differences between the O $_{x}$ results among the models can occur due to the difference in O $_{3}$ that in turn results from the input from the boundaries. Here, CAMx displays an overall high input of O $_{3}$ from the boundary.

Figure 11

Annual mean O $_{x}$ ( $= {NO}_{2} + O_{3}$ ) potential ship impact. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{x}$ potential ship impact, referring to the whole model domain.

[Figure omitted. See PDF]

3.3

NO $_{x}$ spatial distribution

To gain further insight into the differences in the lifetime of NO $_{2}$ in the models, NO $_{x}$ ( $= NO + {NO}_{2}$ ) was calculated and displayed (Appendix E). Differences in NO $_{x}$ provide suggestions regarding the lifetimes because of the reaction of NO $_{2}$ with OH to HNO $_{3}$ . The latter forms ammonium nitrate aerosol together with ammonia; thus, NO $_{2}$ is no longer in the gaseous phase. Another explanation is the dry deposition of NO $_{2}$ , which also causes a loss and consequently differences in the NO $_{x}$ pattern due to different deposition mechanisms. The spatial distribution of the annual mean NO $_{x}$ and potential ship impact on the total NO $_{x}$ concentration have shown a very similar pattern as for NO $_{2}$ . The values of CAMx and CHIMERE are within one range, displaying higher values compared to CMAQ, EMEP and LOTOS-EUROS. These three models show results within one range.

To see the chemical fate of NO $_{2}$ the dry deposition could provide an indication and will be considered in the following Sect. 3.4.

3.4 Dry deposition

In the present study, dry deposition of NO $_{2}$ and O $_{3}$ are displayed for the base and the no-ship case for CAMx, CHIMERE, CMAQ and LOTOS-EUROS. EMEP does not deliver separate NO $_{2}$ and O $_{3}$ deposition files but does deliver oxidized and reactive nitrogen. Thus, EMEP is not considered in this chapter.

3.4.1

Dry deposition of NO $_{2}$

The annual mean NO $_{2}$ dry deposition of all four compared CTMs displays similar values over land areas (Fig. 12). In cities and densely populated regions, all models show high NO $_{2}$ dry deposition, with values over 300 mg m $^{- 2}$ yr $^{- 1}$ . Nevertheless, the frequency distribution of all values shows that this is mainly the case for CAMx and LOTOS-EUROS. Additionally, over the sea, the pattern of annual mean dry deposition of NO $_{2}$ is also similar for CAMx and LOTOS-EUROS.

Figure 12

Annual total dry deposition of NO $_{2}$ . (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean NO $_{2}$ dry deposition, referring to the whole model domain.

[Figure omitted. See PDF]

Table 7 shows that the correlation was strongest between CHIMERE and CAMx ( $R = 0.72$ ). Similarities and strong correlations in the output of both models were also found for the NO $_{2}$ concentration in Sect. 3.1.2. This can be traced back to the same meteorology data that were used by both CTMs.

Table 7

Correlation between models for the whole domain (all grid cells) based on daily data for NO $_{2}$ total dry deposition.

All	CAMx	CHIMERE	CMAQ	LOTOS-EUROS
LOTOS-EUROS	0.48	0.55	0.22	–
CMAQ	0.22	0.27	–
CHIMERE	0.72	–
CAMx	–

The relative potential ship impact on the annual dry deposition of NO $_{2}$ is displayed in Fig. 13. The lowest potential ship impact on NO $_{2}$ dry deposition is simulated by CMAQ and LOTOS-EUROS. In particular, CMAQ shows large areas with negative ( $- 2.5$ %) potential ship impacts over land. The CHIMERE simulations looks similar to the CAMx simulations over land. Along the coastline, CMAQ and LOTOS-EUROS show a potential impact of ships between 10 % and 25 %; CAMx and CHIMERE expect a potential ship impact on the total annual deposition of 25 % to 75 %. The highest potential impact is displayed by CAMx.

Figure 13

Annual mean dry deposition of NO $_{2}$ relative potential ship impact. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean NO $_{2}$ dry deposition potential ship impact, referring to the whole model domain.

[Figure omitted. See PDF]

Differences in NO $_{2}$ dry-deposition model results can be due to the dry-deposition velocities but also due to the different meteorology data used by the models (Wichink Kruit et al., 2014). Dry-deposition velocities of NO $_{2}$ (Fig. S18) show that deposition velocities of CHMIERE and CMAQ are within one range and are lower compared to CAMx and LOTOS-EUROS deposition velocities. Velocities of the latter two are within one range. High velocities might lead to higher deposition rates, leading to high annual mean deposition. This is reflected in the annual dry deposition of NO $_{2}$ , where CAMx and LOTOS-EUROS simulate the highest values. Overall, the models have more differences in NO $_{2}$ dry deposition than in air concentration. As was the case for NO $_{2}$ concentration, CAMx simulated the highest values in dry deposition. The lowest values in NO $_{2}$ dry deposition are displayed by CMAQ. In addition, the correlation between CMAQ and the other models was lowest.

High NO $_{2}$ deposition over water areas caused by ships contributes to eutrophication (Vivanco et al., 2018). A study by Im et al. (2013) showed values of approximately 500 kg (N) m $^{- 2}$ yr $^{- 1}$ ( $\overset{\land}{=} 50 000$ mg m $^{- 2}$ yr $^{- 1}$ ) over the Mediterranean Sea, which means an exceedance of the critical load of 2 g to 3 g (N) m $^{- 2}$ yr $^{- 1}$ ( $\overset{\land}{=}$ 2000 to 3000 mg m $^{- 2}$ yr $^{- 1}$ ) to marine and coastal habitats (Bobbink and Hettelingh, 2011). The present study focused on NO $_{2}$ dry deposition; thus, a direct comparison with critical load levels or with other studies regarding total N deposition would not be possible. A subsequent calculation of N showed that the simulated values in the present study do not exceed the critical loads (Appendix F). Nevertheless, NO $_{2}$ dry deposition from ships contributes to the total N deposition budget, thus increasing with ship traffic and affecting the ecosystems in the Mediterranean Sea.

3.4.2

Dry deposition O $_{3}$

Dry deposition is a major sink for O $_{3}$ in the lowest model layer. O $_{3}$ has high destruction rates on vegetated surfaces through plant stomata and lower rates on surfaces such as water or snow (Clifton et al., 2020). Spatial patterns of annual total O $_{3}$ dry deposition confirm this distribution. Over sea annual totals are lower (250 to 1000 mg m $^{- 2}$ yr $^{- 1}$ ) compared to values over land (2500 to 10 000 mg m $^{- 2}$ yr $^{- 1}$ ; Fig. 14). The correlation for the annual total concentration of O $_{3}$ dry deposition is highest between CHIMERE and CAMx, showing a moderate correlation ( $R = 0.57$ ; Table 8).

Figure 14

Annual total dry deposition of O $_{3}$ . (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{3}$ dry deposition, referring to the whole model domain.

[Figure omitted. See PDF]

Table 8

Correlation between models for the whole domain (all grid cells) based on daily data for O $_{3}$ total dry deposition.

All	CAMx	CHIMERE	CMAQ	LOTOS-EUROS
LOTOS-EUROS	0.14	0.42	0.20	–
CMAQ	0.26	0.27	–
CHIMERE	0.57	–
CAMx	–

Figure 15 shows the potential ship impact on the total dry deposition of O $_{3}$ . CMAQ and LOTOS-EUROS are within a similar range, with potential impacts of ships of 5 % to 10 % over water surfaces. The lowest potential impact of $- 5$ % in the main shipping lanes is simulated by CAMx, showing a similar pattern as for the O $_{3}$ potential ship impact. Over land areas, ships contribute to dry O $_{3}$ deposition from 0.25 % to 2.5 %.

Figure 15

Annual mean dry deposition of O $_{3}$ relative potential ship impact. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean O $_{3}$ dry-deposition potential ship impact, referring to the whole model domain.

[Figure omitted. See PDF]

In addition to the impact of O $_{3}$ dry deposition on plant stomata, it is important to explain differences in surface O $_{3}$ concentration results. The O $_{3}$ concentration is sensitive to the deposition velocity (Clifton et al., 2020), which differs among the four CTMs. This can be confirmed by studies comparing deposition schemes, where differences in O $_{3}$ concentration between models are caused by the variety of processes (Clifton et al., 2020). In particular, the variability in deposition velocities across models, as discussed in Sect. 3.3.1, is seen as an originator leading to uncertainties in tropospheric O $_{3}$ (Wild, 2007). Deposition velocities for the models in the present study (Fig. S19) show the lowest velocities for CMAQ. Highest velocities were found for CAMx over land areas. The deposition velocities go along with the annual dry deposition, with high velocities in areas with high dry deposition.

A model comparison study with 15 models by Hardacre et al. (2015) found the greatest differences in total O $_{3}$ dry deposition occurring in areas where deposition velocities and O $_{3}$ concentrations are highest. Additionally, soil moisture has an important impact on O $_{3}$ deposition and concentration. An evaluation study within the CHIMERE model found that especially in southern Europe, where soil is close to the wilting point during summer and affects stomatal opening, O $_{3}$ dry deposition declines (Anav et al., 2018). This in turn affects the concentration of gases in the lower atmosphere and thus has an impact on O $_{3}$ concentrations.

4 Summary and conclusion

The potential impact of ships on air pollution by NO $_{2}$ and O $_{3}$ the Mediterranean Sea region was simulated with five different regional-scale CTMs (CAMx, CHIMERE, CMAQ, EMEP, LOTOS-EUROS). An evaluation of the results for NO $_{2}$ and O $_{3}$ concentrations is presented here. By using different CTMs, a more robust estimate of the potential ship impact on atmospheric concentrations and deposition can be obtained compared to single CTM runs.

The emission data, simulated year and domain were the same for all models. The models were run in their standard setup. The CTM simulations were evaluated by comparing the simulated data against the measurements from urban and rural background stations around the Mediterranean Sea.

The focus of the study was the comparison of model simulations concerning the concentration of regulatory pollutants and the calculation of potential ship impacts on air pollution concentrations.

Concerning the results for NO $_{2}$ , the model performance showed differences in the time series among the models, caused by the large grid size and the differences in meteorology. All five CTMs underestimated the actual measured NO $_{2}$ concentration data at most stations, along with results from previous studies (e.g., Karl et al., 2019b; Giordano et al., 2015; Knote et al., 2015). The potential ship impact on the concentration of NO $_{2}$ at the measurement stations over land differed among the models. It varied between 1.0 % and 15.3 % at the presented stations. Mean values of the potential impacts of ships on NO $_{2}$ at several stations in one area, as shown in Figs. S3–S10, display values of up to 48.1 %. This was found in the eastern part of the domain (Fig. S7), where the main shipping routes are close to the shore. Previous studies regarding the North and Baltic seas found similar results because shipping lanes are located closer to the shore and have a higher potential impact on the total NO $_{2}$ concentration in coastal regions (Matthias et al., 2010; Karl et al., 2019b). Nevertheless, over water, the model results in the present study display a potential ship impact $> 85$ % on the main shipping routes. High values are also expected for the African coast since the main shipping route is close, but all measurement stations considered here are in continental Europe; no measurements were available for northern Africa.

The potential ship impact was similar to the annual mean concentration of NO $_{2}$ . In both cases, CAMx and CHIMERE displayed the highest annual mean concentration and highest relative potential ship impact. CMAQ, EMEP and LOTOS-EUROS simulated values within one range, which could be confirmed by similarities in the respective frequency distributions.

A relatively good model performance for O $_{3}$ was shown by all five CTMs, but the simulations differed in spatial distribution and potential ship impact over water. An overestimation of simulated O $_{3}$ concentrations was found at almost all stations. The overestimation of actual measured O $_{3}$ by the models agrees with results found in other studies (Appel et al., 2017; Im et al., 2015a, b). Although boxplots for annual mean values of O $_{3}$ vary, for relative potential ship impact they show that CHIMERE, CMAQ, EMEP and LOTOS-EUROS are within the same range. The relative potential impact of ships on total O $_{3}$ decreases to $- 20$ % in areas with high NO $_{2}$ concentrations in all model outputs but mostly for CAMx. Diurnal cycles did not reveal differences in O $_{3}$ depletion over water and land among the models.

The focus of the second part of the present study was dry deposition of NO $_{2}$ and O $_{3}$ . The motivation to examine the dry deposition of NO $_{2}$ and O $_{3}$ more closely was to potentially explain the model differences found for O $_{3}$ and NO $_{2}$ . Investigations of dry deposition are crucial to explain the conservation of mass and fate of these substances. A connection can be seen between a high concentration and low deposition when the deposition velocity is low. This indicates that the substance stays in the atmosphere for longer (i.e., CHIMERE). On the other hand, if the deposition rate and deposition are high, the concentration is lower (i.e., LOTOS-EUROS).

Regarding NO $_{2}$ dry deposition and the potential ship impact, CAMx showed the highest values. CMAQ displayed the lowest values for NO $_{2}$ dry deposition. Along the shoreline, CMAQ and LOTOS-EUROS reveal a potential ship impact between 10 % and 25 %; CAMx and CHIMERE expect a potential ship impact on total annual NO $_{2}$ dry deposition of 25 % to 75 %, in some regions also along the coast. These differences are caused by mechanisms to calculate dry-deposition velocities, which are unique for each model, as well as differing inputs, such as land use data (Wichink Kruit et al., 2014; Vivanco et al., 2018). The deposition velocities have shown that the highest annual mean NO $_{2}$ dry deposition was found for CAMx and LOTOS-EUROS, which also had the highest deposition velocities.

The potential ship impact on the total dry deposition of O $_{3}$ displays the highest impact with values between 75 % and 85 % simulated with CHIMERE. CMAQ and LOTOS-EUROS are within a similar range, with potential ship impacts mainly 5 % to 10 % over water areas. The lowest potential impact of $- 5$ % in the main shipping lanes was simulated by CAMx. The correlation of the annual total deposition of O $_{3}$ dry deposition was the highest for CHIMERE and CAMx. Nevertheless, low to medium correlation was found for all other models. The deposition velocities for O $_{3}$ dry deposition have shown a similar pattern as for NO $_{2}$ : the highest velocities were simulated by CAMx and LOTOS-EUROS.

In general, more deviations between the dry-deposition model results were found compared to the modeled simulations of the air concentration of pollutants. This is because NO $_{2}$ and O $_{3}$ in the atmosphere are formed more or less “directly” from the emission data, but dry deposition differs because there are other, model-specific mechanisms behind it.

Overall, in the present study the models were run in their standard setup; a complete harmonization was not the goal. Nevertheless, emissions were harmonized to exclude the source of uncertainty coming from the emission input dataset. This was done to shed light on what other factors except the emission data lead to differences between individual model results.

Furthermore, possible limitations and over- and underestimations of model outputs were pointed out through this intercomparison. Large grid sizes can cause errors insofar as in simulations the land areas are not seen as such but as water areas and vice versa. This is especially problematic when having measurement stations located close to the sea. NO $_{2}$ simulations regarding the relative potential ship impact differed more among models compared to the O $_{3}$ simulations. Limitations were traced back to the large grid sizes. In addition, model-specific chemistry mechanisms lead to differences in simulated concentrations.

A more reliable estimate of potential ship impacts on the atmospheric concentration as well as deposition can be achieved through an ensemble mean with standard deviations based on different model results. Previous studies have shown that using only one chemistry transport model leads to statistical bias, underestimations of model uncertainties and overconfidence of results (e.g., Solazzo et al., 2018; Riccio et al., 2012; Solazzo et al., 2018). This indicates that the aim should be to use a model ensemble. This is of importance, especially regarding the policy point of the study: if model simulations should help in decisions for regulations regarding shipping, the uncertainty of single models should be considered. In the present study, the focus was laid on shipping emissions and their impact on NO $_{2}$ and O $_{3}$ concentrations. It was found that the shipping impact in many coastal areas of the Mediterranean Sea is smaller compared to the shipping impact in the North and Baltic seas. This is because the most intensively used shipping lanes are typically further from the coast.

In an additional investigation of potential ship contribution impacts on air pollution, aerosol particles and wet deposition need to be considered, which is the next step in the current intercomparison study. The aerosol formation mechanisms differ among the CTMs; therefore, a detailed investigation of PM $_{2.5}$ and its chemical composition is necessary and will be part of further investigations in the SCIPPER project. Another open question that future studies might answer is the comparison of vertical structures of pollution transport. The present study considered the lowest simulated layer, but also mixing of pollutants to higher layers can deliver explanations for differences in lowest layer concentrations.

Appendix A

Definitions of NMB, $R$ and RMSE

A1 $Normalized mean bias (NMB) = \frac{\sum_{1}^{n} (M - O)}{\sum_{1}^{n} (O)},$ where $M$ and $O$ stand for model and observation results, respectively. The time average is indicated over $n$ time intervals (number of observations). The time average is done for 1 year. A2 $Correlation (R) = \frac{1}{(n - 1)} \sum_{1}^{n} ((\frac{O - \overline{O}}{σ_{o}}) \times (\frac{M - \overline{M}}{σ_{m}}))$ A3 $Root mean square error (RMSE) = \sqrt{\frac{\sum_{1}^{n} (M - O)^{2}}{n}}$ RMSE is a measure of accuracy and allows prediction errors of different models to be compared for a particular dataset.

Appendix B

Table B1

Detailed overview of monitoring stations.

Name	Code	Country	Latitude	Longitude	Elevation	Station type	Data	Measured pollutants
							points
Vlora	al0204a	Albania	40.40309	19.4862	25	urban	6850	benzene, CO, NO $_{2}$ , NO $_{x}$ , O $_{3}$ ,
						background		PM $_{10}$ , PM $_{2.5}$ , SO $_{2}$
Shkoder	al0206a	Albania	42.3139	19.52342	13	urban	7536	CO, NO $_{2}$ , NO $_{x}$ , O $_{3}$ , PM $_{10}$ ,
						background		PM $_{2.5}$ , SO $_{2}$
Els Torms	es0014r	Spain	41.39389	0.73472	470	rural	8549	NO, NO $_{2}$ , NO $_{x}$ , O $_{3}$ , SO $_{2}$
						background
Vila-seca (RENFE)	es1117a	Spain	41.11209	1.151824	41	suburban	8594	NO, NO $_{2}$ , NO $_{x}$
						background
Sant Celoni (Carles Damm)	es1275a	Spain	41.68905	2.495747	145	suburban	7180	NO, NO $_{x}$ , NO $_{2}$ , SO $_{2}$
						background
Barcelona (Ciutadella)	es1679a	Spain	41.38641	2.187417	7	urban	8565	NO, NO $_{2}$ , NO $_{x}$
						background
Mataró (passeig dels Molins)	es1816a	Spain	41.54716	2.443254	40	urban	8484	NO, NO $_{x}$ , NO $_{2}$ , O $_{3}$ , CO
						background
Barcelona (Palau Reial)	es1992a	Spain	41.38748	2.11515	81	urban	8393	NO, NO $_{2}$ , NO $_{x}$ , SO $_{2}$ , CO
						background
Marseille Cinq-Avenues	fr03043	France	43.30607	5.395794	73	urban	8585	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$ , SO $_{2}$
						background
Esterel	fr03070	France	43.43786	6.768366	5	suburban	1820	NO $_{2}$ , O $_{3}$
						background
Agathois-piscénois	fr08022	France	43.28776	3.504831	20	suburban	8382	NO $_{2}$ , O $_{3}$
						background
Gauzy	fr08614	France	43.8344	4.374219	40	urban	8406	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$
						background
Rigaud	fr08713	France	42.68402	2.903453	50	urban	8419	NO $_{2}$ , PM $_{10}$
						background
Cannes Broussilles	fr24009	France	43.5625	7.007222	71	urban	8587	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$
						background
Manosque	fr24018	France	43.83527	5.785831	385	urban	8517	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$
						background
Nice Arson	fr24036	France	43.70207	7.286264	11	urban	8701	NO $_{2,}$ O $_{3}$ , PM $_{10}$ , PM $_{2.5}$
						background
Ajaccio Sposata	fr41007	France	41.94923	8.757586	60	suburban	8497	NO $_{2}$ , O $_{3}$
						background
Bastia Montesoro	fr41017	France	42.67134	9.434644	47	rural	8626	NO $_{2}$ , O $_{3}$ , PM $_{2.5}$
						background
Lykovrysi	gr0035a	Greece	38.06963	23.77689	210	suburban	6719	NO $_{2}$ , NO $_{2}$ , O $_{3}$
						background
Neochorouda	gr0045a	Greece	40.73984	22.87623	229	suburban	8725	NO $_{2}$ , NO, O $_{3}$
						background
Finokalia	gr0002r	Greece	35.315871	25.666216	250	rural	6825	PM $_{10}$ , O $_{3}$
						background
NA	hr0025a	Croatia	44.86247	13.81686	0	suburban	8293	NO $_{2}$ , NO $_{x}$ , O $_{3}$
						background
Melilli	it0611a	Italy	37.18237	15.12883	300	urban	7964	NO $_{2}$ , O $_{3}$ , SO $_{2}$
						background

Table B1

Continued.

Name	Code	Country	Latitude	Longitude	Elevation	Station type	Data	Measured pollutants
							points
Priolo	it0614a	Italy	37.15612	15.19087	35	urban	7902	NO $_{2}$ , benzene, SO $_{2}$
						background
SR – Via Gela	it0620a	Italy	37.10247	15.26564	60	suburban	6958	NO $_{2}$ , O $_{3}$ , SO $_{2}$
						background
Gela – Enimed	it0815a	Italy	37.06222	14.28422	13	suburban	8052	NO $_{2}$ , SO $_{2}$ , benzene
						background
Aprilia	it0865a	Italy	41.59528	12.65361	83	urban	8169	NO $_{2}$
						background
Leonessa	it0989a	Italy	42.5725	12.96194	948	urban	8207	NO $_{2}$ , O $_{3}$
						background
Gherardi	it1179a	Italy	44.83972	11.96111	$- 2$	rural	8269	NO $_{x}$ , NO $_{2}$ , O $_{3}$
						background
Adria	it1213a	Italy	45.04667	12.06194	4	urban	8306	NO $_{2}$ , NO $_{x}$ , O $_{3}$
						background
Cennm1	it1375a	Italy	39.44361	9.015278	124	rural	7595	NO $_{2}$ , SO $_{2}$
						background
Teatro d'Annunzio	it1423a	Italy	42.45639	14.23472	4	urban	8135	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$ , SO $_{2}$ ,
						background		benzene, CO
Cenps7	it1576a	Italy	39.20333	8.386111	25	suburban	7968	CO, NO $_{2}$ , SO $_{2}$
						background
Taranto San Vito	it1610a	Italy	40.42333	17.22528	10	urban	7871	NO $_{2}$
						background
Lecce – S. M. Cerrate	it1665a	Italy	40.45889	18.11611	10	rural	7290	NO $_{2}$ , O $_{3}$
						background
Brindisi Via Magellano	it1702a	Italy	40.65083	17.94361	10	suburban	7904	NO $_{2}$ , PM $_{10}$
						background
Genga – Parco Gola della Rossa	it1773a	Italy	43.46806	12.95222	550	rural	5310	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$ , SO $_{2}$ ,
						background		benzene, CO
Civitanova Ippodromo S. Marone	it1796a	Italy	43.33556	13.67472	110	rural	6699	NO $_{2}$ , NO $_{x}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$ ,
						background		benzene
Guardiaregia	it1806a	Italy	41.41889	14.52556	884	rural	7892	NO $_{2}$ , NO $_{x}$ , O $_{3}$ , SO $_{2}$
						background
Ancona Cittadella	it1827a	Italy	43.61167	13.50861	100	urban	5985	NO $_{2}$ , O $_{3}$ , PM $_{10}$ , PM $_{2.5}$ ,
						background		benzene, CO, SO $_{2}$
Schivenoglia	it1865a	Italy	44.99694	11.07083	16	rural	8325	NO $_{2}$ , NO $_{x}$ , O $_{3}$ , SO $_{2}$ , benzene
						background
Trapani	it1898a	Italy	38.01237	12.54689	40	urban	7396	NO $_{2}$ , O $_{3}$ , benzene, CO
						background
San Rocco	it1914a	Italy	44.87306	10.66389	22	rural	8398	NO $_{2}$ , NO $_{x}$ , O $_{3}$
						background
Locri	it1940a	Italy	38.22976	16.25518	11	urban	8509	NO $_{2}$ , O $_{3}$ , SO $_{2}$ , benzene, CO
						background
GR – Maremma	it1942a	Italy	42.67056	11.09417	40	rural	7784	NO $_{2}$ , O $_{3}$
						background
Censa3	it1947a	Italy	39.06667	9.008889	56	urban	8169	NO $_{2}$ , SO $_{2}$ , benzene
						background
Milazzo – Termica	it1997a	Italy	38.19061	15.24911	28	suburban	8329	NO $_{2}$ , O $_{3}$ , CO, benzene
						background
Stadio Casardi	it2003a	Italy	41.31667	16.28611	15	urban	8391	NO $_{2}$ , O $_{3}$ , benzene
						background

Table B1

Continued.

Name	Code	Country	Latitude	Longitude	Elevation	Station type	Data	Measured pollutants
							points
Cenqu1	it2040a	Italy	39.23278	9.188056	8	urban	8181	NO $_{2}$ , O $_{3}$ , SO $_{2}$ ,
						background		benzene
Carbonara	it2051a	Italy	41.07694	16.86583	130	suburban	7505	NO $_{2}$ , PM $_{10}$
						background
Ceglie Messapica	it2148a	Italy	40.64917	17.5125	100	suburban	8393	NO $_{2}$ , PM $_{10}$ , PM $_{2.5}$ ,
						background		SO $_{2}$ , CO, benzene
LI – Piombino-Parco-VIII-Marzo	it2154a	Italy	42.93194	10.52417	40	urban	8228	NO $_{2}$ , benzene
						background
Gela – Biviere	it2206a	Italy	37.02249	14.34497	0	rural	8277	NO $_{2}$ , O $_{3}$ , SO $_{2}$
						background
Bar2	me0008a	Montenegro	42.10035	19.10348	12	urban	7721	CO, NO, NO $_{2}$ , NO $_{x}$ ,
						background		O $_{3}$ , SO $_{2}$
Niskic2	me0009a	Montenegro	42.78121	18.94291	629	urban	7693	CO, NO, NO $_{2}$ , NO $_{x}$ ,
						background		O $_{3}$ , SO $_{2}$
Koper	si0038a	Slovenia	45.54297	13.71354	56	urban	8198	NO $_{2}$ , NO $_{x}$ , O $_{3}$
						background
Balikesir-Bandirma	tr100241	Turkey	40.34795	27.97496	38	urban	8509	NO $_{2}$
						background
Canakkale-Lapseki	tr170313	Turkey	40.40307	26.77063	12	rural	8170	NO $_{2}$ , NO $_{x}$ , O $_{3}$ ,
						background		PM $_{2.5}$ , SO $_{2}$
Istanbul-Esenyurt	tr340241	Turkey	41.02028	28.66955	36	urban	7915	NO $_{2}$ , NO $_{x}$ , SO $_{2}$
						background
Istanbul-Sultangazi	tr340841	Turkey	41.10197	28.87202	128	urban	8304	NO $_{2}$ , NO $_{x}$ , SO $_{2}$
						background
Kirkareli-Luleburgaz	tr390441	Turkey	41.39841	27.34588	56	rural	8393	NO $_{2}$ , SO $_{2}$
						background

Appendix C

Example time series for NO $_{2}$

Figure C1

Time series with daily mean NO $_{2}$ concentrations in 2015 at station fr08614 in France. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.23$ ; CHIMERE $= 0.20$ ; CMAQ $= 0.60$ ; EMEP $= 0.02$ ; LOTOS-EUROS $= 0.65$ . Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Figure C2

Time series with daily mean NO $_{2}$ concentration in 2015 at station it1773a in Italy. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.03$ ; CHIMERE $= 0.03$ ; CMAQ $= 0.20$ ; EMEP $= - 0.09$ ; LOTOS-EUROS $= 0.14$ Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Figure C3

Time series with daily mean NO $_{2}$ concentration in 2015 at station gr0035a in Greece. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.15$ ; CHIMERE $= 0.20$ ; CMAQ $= 0.28$ ; EMEP $= 0.55$ ; LOTOS-EUROS $= 0.38$ . Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Appendix D

Example time series for O $_{3}$

Figure D1

Time series with daily mean O $_{3}$ concentration in 2015 at station fr08614 in France. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.57$ ; CHIMERE $= 0.6$ ; CMAQ $= 0.71$ ; EMEP $= 0.39$ ; LOTOS-EUROS $= 0.78$ . Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Figure D2

Time series with daily mean O $_{3}$ concentration in 2015 at station it1773a in Italy. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.37$ ; CHIMERE $= 0.4$ ; CMAQ $= 0.58$ ; EMEP $= 0.35$ ; LOTOS-EUROS $= 0.7$ . Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Figure D3

Time series with daily mean O $_{3}$ concentration in 2015 at station gr0035a in Greece. The black triangle on the map (bottom right) displays the location of the station. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) EMEP; (e) LOTOS-EUROS. Dashed gray line – measured data; colored lines – modeled data; gray line – modeled potential ship impact. Correlation between modeled and measured data for hourly total emission data for 2015: CAMx $= 0.29$ ; CHIMERE $= 0.46$ ; CMAQ $= 0.50$ ; EMEP $= 0.71$ ; LOTOS-EUROS $= 0.57$ . Ship $_{a}$ : potential absolute ship impact; Ship $_{r}$ : potential relative ship impact of the respective model.

[Figure omitted. See PDF]

Appendix E

NO $_{x}$ spatial distribution

Figure E1

Annual mean of NO $_{x}$ total concentration. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean NO $_{x}$ concentration, referring to the whole model domain.

[Figure omitted. See PDF]

Figure E2

Annual mean relative potential ship impact of NO $_{x}$ . (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS. Below the domain figure the respective frequency distribution is displayed for the annual mean relative potential ship impact of NO $_{x}$ , referring to the whole model domain.

[Figure omitted. See PDF]

Appendix F Annual total dry deposition of N

Figure F1

Annual total dry deposition of N. (a) CAMx; (b) CHIMERE; (c) CMAQ; (d) LOTOS-EUROS.

[Figure omitted. See PDF]

Code and data availability

CAMx source code and documentation can be downloaded from https://camx-wp.azurewebsites.net/download/source/ (last access: 19 January 2023; Ramboll, 2023) and the Chimere website (https://www.lmd.polytechnique.fr/chimere/2020_getcode.php, last access: 19 January 2023). CMAQ version 5.2, which was used here, is available at 10.5281/zenodo.1167892 (US EPA Office of Research and Development, 2017). EMEP is available at 10.5281/zenodo.3647990 (EMEP MSC-W, 2020), LOTOS-EUROS is available at https://lotos-euros.tno.nl/open-source-version/ (last access: 19 January 2023; TNO, 2023), and WPS/WRF is available from WPS (2022; https://github.com/wrf-model/WPS, last access: 19 January 2023) and WRF Community (2000, 10.5065/D6MK6B4K). The COSMO software is available at https://www.cosmo-model.org/content/support/software/default.htm#models (last access: 24 January 2023; COSMO, 2023) and ecmwf-ifs/ifs-scripts at https://github.com/ecmwf-ifs (last access: 19 January 2023; ECMWF, 2023).

Data on measurement stations from EEA can be downloaded at https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm (last access: 20 January 2023). CTM model results are available upon request.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-1825-2023-supplement.

Author contributions

LF: CMAQ model runs, evaluation and analysis of model results, preparation and writing of the paper. MK: Analysis of the results, revision of the text. VM: Supervision, analysis of the results, revision of the text. SO: CAMx and CHIMERE model runs, discussion of the results. RK and JK: LOTOS-EUROS model runs, land-based emissions data provision, discussion of the results. JM and SJ: EMEP model runs, discussion of the results. JPJ and EM: STEAM model runs, shipping emissions data provision, discussion of the results.

Competing interests

The contact author has declared that none of the authors has any competing interests.

Disclaimer

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

This was work supported by the SCIPPER project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 814893.

The Community Multiscale Air Quality Modeling System (CMAQ) is developed and maintained by the USEPA. Its use is gratefully acknowledged. Ronny Petrik from Helmholtz Centre Hereon is acknowledged for providing meteorology and boundary conditions for CMAQ runs.

The computations for the regional modeling using the EMEP model was enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.

Support from the Meteorological Synthesizing Center–West of EMEP at the Norwegian Meteorological Institute, especially from Peter Wind and David Simpson, for the implementation of emissions and meteorological fields used in this paper in the EMEP open-source model is gratefully acknowledged.

Financial support

This research has been supported by the European Union's Horizon 2020 (SCIPPER (grant no. 814893)) and the Swedish Research Council (grant agreement no. 2018-05973).The article processing charges for this open-access publication were covered by the Helmholtz-Zentrum Hereon.

Review statement

This paper was edited by Andrea Pozzer and reviewed by two anonymous referees.

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Abstract

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Shipping has a significant share in the emissions of air pollutants such as NO $_{x}$ and particulate matter (PM), and the global maritime transport volumes are projected to increase further in the future. The major route for short sea shipping within Europe and the main shipping route between Europe and East Asia are found in the Mediterranean Sea. Thus, it is a highly frequented shipping area, and high levels of air pollutants with significant potential impacts from shipping emissions are observed at monitoring stations in many cities along the Mediterranean coast.

The present study is part of the EU H2020 project SCIPPER (Shipping contribution to Inland Pollution Push for the Enforcement of Regulations). Five different regional chemistry transport models (CAMx – Comprehensive Air Quality Model with Extensions, CHIMERE, CMAQ, EMEP – European Monitoring and Evaluation Programme, LOTOS-EUROS) were used to simulate the transport, chemical transformation and fate of atmospheric pollutants in the Mediterranean Sea for 2015. Shipping emissions were calculated with the Ship Traffic Emission Assessment Model (STEAM) version 3.3.0, and land-based emissions were taken from the CAMS-REG v2.2.1 dataset for a domain covering the Mediterranean Sea at a resolution of 12 km $\times$ 12 km (or $0.1^{\circ} \times 0.1^{\circ}$ ). All models used their standard setup for further input. The potential impact of ships was calculated with the zero-out method. The model results were compared to each other and to measured background data at monitoring stations.

The model results differ regarding the time series and pattern but are similar concerning the overall underestimation of NO $_{2}$ and overestimation of O $_{3}$ . The potential impact from ships on the total NO $_{2}$ concentration was especially high on the main shipping routes and in coastal regions (25 % to 85 %). The potential impact from ships on the total O $_{3}$ concentration was lowest in regions with the highest NO $_{2}$ impact (down to $- 20$ %). CAMx and CHIMERE simulated the highest potential impacts of ships on the NO $_{2}$ and O $_{3}$ air concentrations. Additionally, the strongest correlation was found between CAMx and CHIMERE, which can be traced back to the use of the same meteorological input data. The other models used different meteorological input due to their standard setup. The CMAQ-, EMEP- and LOTOS-EUROS-simulated values were within one range for the NO $_{2}$ and O $_{3}$ air concentrations. Regarding simulated deposition, larger differences between the models were found when compared to air concentration. These uncertainties and deviations between models are caused by deposition mechanisms, which are unique within each model. A reliable output from models simulating ships' potential impacts can be expected for air concentrations of NO $_{2}$ and O $_{3}$ .

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Title

Potential impact of shipping on air pollution in the Mediterranean region – a multimodel evaluation: comparison of photooxidants NO2 and O3

Author

Fink, Lea¹

; Karl, Matthias¹; Volker, Matthias¹

; Oppo, Sonia²; Kranenburg, Richard³; Kuenen, Jeroen³

; Moldanova, Jana⁴

; Jutterström, Sara⁴; Jalkanen, Jukka-Pekka⁵

; Majamäki, Elisa⁵

¹ Institute of Coastal Environmental Chemistry, Helmholtz Centre Hereon, 21502 Geesthacht, Germany
² AtmoSud, Air Quality Observatory in the Provence-Alpes-Côte d'Azur region, 13006 Marseille, France
³ TNO, Netherlands Organization for Applied Scientific Research, 3584 CB Utrecht, the Netherlands
⁴ IVL, Swedish Environmental Research Institute, 411 33 Göteborg, Sweden
⁵ FMI, Finnish Meteorological Institute, 00560 Helsinki, Finland

Pages

1825-1862

Publication year

2023

Publication date

2023

Publisher

Copernicus GmbH

ISSN

16807316

e-ISSN

16807324

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.5194/acp-23-1825-2023

ProQuest document ID

2771625782

Potential impact of shipping on air pollution in the Mediterranean region – a multimodel evaluation: comparison of photooxidants NO2 and O3

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