Regional chemistry transport model simulations with the Community Multiscale Air Quality (CMAQ) model v5.0.1 (; ; ) were performed to assess the effect of emissions from ship traffic on the present-day and future air qualities of the Baltic Sea region. The CMAQ model computes the air concentration and deposition fluxes of atmospheric gases and aerosols as a consequence of emission, transport and chemical transformation. The atmospheric chemistry of reactive species is treated by the Carbon Bond V mechanism , with updated toluene chemistry and chlorine radical chemistry (mechanism cb05tucl; ).

The aerosol scheme AERO5 is used for the formation of secondary inorganic aerosol (SIA). Aerosol growth and nucleation is simulated by three log-normal distributed modes, each represented by three moments . The Aitken and accumulation modes represent PM2.5 and the coarse mode represents particulate matter with diameter >2.5 µm (PMcoarse). The instantaneous gas-phase–aerosol equilibrium partitioning of sulfuric acid (H2SO4), HNO3, hydrochloric acid (HCl) and NH3 on the fine-particle modes is solved with the ISORROPIA v1.7 mechanism . Dynamic mass transfer is simulated for the coarse particle mode because large particles often do not reach equilibrium with the gas phase for typical atmospheric timescales . For the coarse mode, semi-volatile inorganic species are allowed to condense and evaporate, while H2SO4 does not evaporate again from the coarse mode. Because of the dynamic mass transfer to coarse particles it is possible to use CMAQ for the simulation of chloride (Cl-) replacement by NO3- in mixed marine and urban air masses , which could be an important aerosol process in the Baltic Sea region.

Sea salt emissions were calculated in line by the parameterization of , as described in . Sea salt surf zone emissions were deactivated because of considerable overestimations in some coastal regions . The formation of secondary organic aerosol (SOA) from isoprene, monoterpenes, sesquiterpenes, benzene, toluene, xylene and alkanes (; ) is included. SOA formation pathways include the traditional two-product representation, reaction of volatile organic compounds (VOCs) to give non-volatile products, oxidative ageing of primary organic aerosol, acid-catalysed enhancement of SOA mass, oligomerization reactions and in-cloud aqueous-phase oxidation.

Three types of clouds are modelled in CMAQ: subgrid convective precipitation clouds, subgrid non-precipitating clouds and grid-resolved clouds. CMAQ simulates the aqueous-phase chemistry in all cloud types. For the two types of subgrid clouds, the cloud module in CMAQ vertically redistributes pollutants and calculates in-cloud and precipitation scavenging. Since the meteorological model provides information about the grid-resolved clouds, CMAQ subsequently does not apply further cloud dynamics for this cloud type. Subgrid clouds are only simulated in CMAQ when the meteorological driver uses a convective cloud parameterization. Hence subgrid clouds are treated by CMAQ on the coarser outer-resolution grids (16 and 64 km) but not on the 4×4 km2 model domain because the convective clouds are resolved for the fine-grid resolution by the meteorological model.

Wet deposition of gases and particles is computed by the resolved cloud model of CMAQ, which estimates how much certain vertical model layers contributed to the precipitation. The precipitation flux for each model layer is computed as a function of the non-convective precipitation rate, the sum of hydrometeors (rain, snow and graupel) and the layer thickness (see for details).

Dry deposition is determined as the product of the atmospheric concentration and the deposition velocity. The dry-deposition velocity is modelled in CMAQ using the resistance analogy, where resistances are defined along pathways from the atmosphere to the surface, which act in parallel or in series. Details on the deposition pathways in CMAQ can be found in . The deposition velocity for particles is calculated based on the aerosol size distribution, as well as meteorological and land-use information. For large particles, the dry-deposition transfer is by turbulent air motion and by direct gravitational sedimentation. The dry-deposition algorithm for particles includes an impaction term in the coarse mode and the accumulation mode.

In the resistance method it is assumed that the surface concentration of the chemical species is zero. However, NH3 can be both emitted from and deposited to surfaces depending on its atmospheric concentration. This bidirectional nature of the air–surface exchange can modify the atmospheric transport and environmental impact of ammonia. Bidirectional fluxes of NH3 over marine surfaces have been documented in a review by . In fact, inclusion of the bidirectional air–water exchange in a CTM resulted in lower overall dry deposition of NH3 to coastal waters . However, until now, the parameterization of the bidirectional flux has not been evaluated to a large extent for marine waters. Although the bidirectional flux of NH3 is implemented in CMAQ v5.0.1, the option was not used in this study. Because we are mainly interested in the differences in total nitrogen deposition due to changes in emission alone, the outcome of this study will be less affected by the sensitivity of the modelled nitrogen deposition to bidirectional fluxes of ammonia.

Nested simulations with CMAQ were performed on a horizontal resolution of 4×4 km2 to simulate the current and future air quality situation for the entire Baltic Sea region. The model was set up on a 64×64 km2 grid for the whole of Europe, subsequently on an intermediate nested 16×16 km2 grid for northern Europe, and finally on two nested 4×4 km2 grids, one for the southern Baltic Sea (Baltic major) and one for the northern Baltic Sea (including Bothnian Bay and Gulf of Finland). The nesting is visualized in Fig. a and the geographic details of the high-resolution domain is shown in Fig. b. The vertical dimension of the model extends up to 100 hPa in a sigma hybrid pressure coordinate system with 30 layers. Twenty of these layers are below approximately 2 km; the lowest layer extends to ca. 36 m above ground. A spin-up period of 1 month (December 2011) was used for the initialization of the model runs, which is sufficiently long to prevent initial conditions having an effect on the simulated atmospheric concentrations of the investigated period (year 2012).

The meteorological fields that drive the CTM were simulated with the COSMO-CLM, version 5.0, for the year 2012 , using the ERA-Interim reanalysis and spectral nudging technique to force the model. COSMO itself is the operational weather forecast model applied and further developed by a consortium of national weather services, whereas COSMO-CLM stands for the climate mode used and developed by the limited-area modelling community (clm-community; ).

The meteorological runs were performed first on a 0.11×0.11∘ rotated lat–long grid using 40 vertical layers up to 22 km for the whole of Europe. The output was used as the forcing of a high-resolution nested meteorology run on a 0.025×0.025∘ grid; 50 vertical levels were used for this simulation for the Baltic Sea region. The convection permitting configuration is used on the high-resolution grid, e.g. only shallow convection is based on the Tiedtke scheme, resolving convective precipitation clouds. The meteorological fields were processed afterwards using a modified version of CMAQ's Meteorology-Chemistry Interface Processor (MCIP; ) to match the extension, resolution and projection of the CMAQ nested grids.

Based on the temperature anomalies and precipitation anomalies for the decade 2004–2014 for Baltic Proper, the year 2012 was chosen as the meteorological reference year for the CTM simulations. Year 2012 anomalies for 2 m temperature (±2 ∘C) and total precipitation (±25 mm) were closely aligned with the decadal average of the 2004–2014 period. The meteorological year 2012 was also used in CTM calculations of the future air quality situation to avoid complication of the interpretation of changes between the present-day and the future. Hence, future changes in the air quality are solely due to changed land-based and shipping emissions.

The initial conditions for the simulation and the lateral boundary conditions for the 64×64 km2 outer European domain (CD64) are taken from APTA global reanalysis and were provided by the Finnish Meteorological Institute (FMI). The global boundary conditions results have been interpolated in time and space to provide hourly boundary conditions for the outer domain. Boundary conditions for the nested intermediate grid and the two inner grids were calculated on an hourly basis from the output of the next-outer grid. For the model simulations with no shipping emissions, the full model chain was run again with all emissions except for those from ship traffic in all the CMAQ grids.

Hourly gridded emissions of NOx, sulfur oxides (SOx=SO2+SO3), carbon monoxide (CO), NH3, PM2.5, PMcoarse and non-methane volatile organic compounds (NMVOCs) were calculated for the year 2012 using the comprehensive European emission model SMOKE-EU, which is an adaptation of the US-EPA SMOKE (Sparse Matrix Operator Kernel Emissions) model . NMVOC emissions were speciated according to the carbon bond mechanism (cb5) (; ) of PM2.5 emissions according to the AERO5 aerosol mechanism. The SMOKE-EU emission data are based on reported annual total emissions from the European point source emission register (EPER), the official EMEP emission inventory and the EDGAR HTAP v2 database (; ; ). SMOKE-EU distinguishes 10 major source sectors (including a number of subsector definitions) according to the Selected Nomenclature for sources of Air Pollution (SNAP) of the European Environmental Agency (EEA) (Table ). For all point sources explicit plume rise calculations based on real-world stack information were performed .

The annual total emissions were temporally and spatially redistributed individually for each emission sector and grid cell. Emissions of residential heating were redistributed using the heating demand calculated from daily average temperatures . Emissions from agricultural activity and animal husbandry were disaggregated according to a fertilizer and plant growth model and meteorological parameters . Finally, biogenic emissions were calculated offline with the biogenic Emission Inventory System BEIS version 3.4 (; ). The SMOKE-EU emission datasets were calculated on a 5×5 km2 grid for the whole of Europe and were subsequently interpolated to the respective CMAQ model grids.

Shipping emissions for the Baltic Sea and North Sea with high spatial and temporal resolution for this study were obtained from STEAM (; ). STEAM combines the AIS-based information and the detailed technical knowledge of the world fleet with principles of naval architecture. This input information is used to predict the resistance of vessels in water and the instantaneous engine power of the main and auxiliary engines on a minute-by-minute basis for each vessel that has sent AIS messages. The model predicts as output both the instantaneous fuel consumption and the emissions of selected pollutants. The dynamic modelling of shipping emissions also includes, for example, the emission control areas and regulations, emission abatement equipment on board the ships as well as fuel sulfur content modelling separately for the main and auxiliary engines (; ).

Detailed vessel characteristics have been gathered for more than 90 000 individual ships, reported by IHS Fairplay and other ship classification societies. The AIS system provides automatic updates of the positions and instantaneous speeds of ships at intervals of a few seconds. For this study, archived and downsampled (approx. 4 min update rate) AIS messages provided by the Baltic Sea riparian states were used for 2012 and 2014, containing several hundred million AIS messages annually.

The shipping emission inventory consist of hourly updated 2×2 km2 gridded data for NOx, SOx, CO and particulate matter, which is further divided into elementary carbon (EC), organic carbon (OC), sulfate (SO4) and mineral ash. For the North Sea, ship emissions from 2011 were adopted for 2012; total ship emissions of NOx were almost unchanged between the 2 years. For Baltic Sea ship emissions are from 2012 and were provided for two vertical layers (below 36 m, from 36 to 1000 m). In CMAQ, SOx was attributed completely to SO2 and a NO:NO2 ratio of 95 : 5 was applied. Ship emissions below 36 m were attributed to the lowest vertical model layer. Ship emissions above 36 m were attributed to the second lowest layer, which appears to be justified based on findings with ship plume simulations , showing that plume dispersion in the convective boundary layer (BL) is insensitive to the initial buoyancy flux.

Shipping in the Baltic Sea in the future is modelled in a number of scenarios taking into account the development of traffic and transport work, fleet development for different ship types (number and size), changes in fuel mixture and regulations influencing emissions and fuel consumption. Due to the long lifetime of ships it will take about 30 years after the NECA entry date until the entire ship fleet is renewed and follows the Tier 3 emission regulation for NOx. It was decided that the future regional CTM simulations for 2040 would be performed in order to see the full effect of the NECA.

The three scenarios studied here (BAU, NoNECA and EEDI) for future shipping emissions are combined with land-based emissions for 2040, which follow the currently decided emission regulations in Europe. The future land-based emission dataset for the year 2040 was created based on the present-day SMOKE-EU emission dataset (Sect. ) using growth factors for each source sector and each species. The employed emission scaling factors are based on the trend between annual total emissions from the 2012 SMOKE-EU inventory and 2040 baseline emissions of the current legislation (CLE) scenario from ECLIPSE v5 . CLE assumes efficient enforcement of committed legislation but delays in introducing or enforcing particular laws are considered when such information was available. The scaling factors for land-based emissions, given as average of the Baltic Sea riparian states for CO, PM-other, SO4, SO2, NOx and NH3 are 0.75, 0.70, 0.45, 0.45, 0.40 and 0.80, respectively.

Ship emissions from the STEAM database were merged with the land-based emissions from the SMOKE-EU database for the Baltic Sea region and interpolated to the corresponding CMAQ domain sizes and resolutions. Total annual emissions of NOx in 2012 and in 2040 (BAU scenario) prepared for the CMAQ simulations are shown on geographic maps in Fig. .

CMAQ model results for surface air concentrations of O3, NO2, SO2 and PM2.5 from the 4 km resolution grid were evaluated against measurements at regional background stations of the EMEP monitoring programme available from the EBAS database. The evaluation was done for the entire year 2012 and separately for summer (JJA) 2012. Details on the methodology for comparing modelled air pollutant concentrations with observations are given in Sect. S3.

The difference between the two future scenarios BAU 2040 and NoNECA 2040 is the higher emission reduction in NOx from shipping in the BAU scenario through the establishment of the NECA. Figure  illustrates the effect of introducing the NECA in 2021 into major air quality components compared to a future situation without NECA, determined based on the difference between modelled concentrations in the BAU 2040 and NoNECA 2040 scenarios. Land-based emissions are the same in both scenarios; therefore changes are solely due to different ship emissions in the two future scenarios.

The result of the NECA in 2040 is a reduction in NOx emissions from shipping by 59 % on average, corresponding to the difference between a Tier 3-dominated ship fleet with the NECA and Tier 2-dominated ship fleet without the NECA. The reduction in NOx emissions from shipping primarily translates into a ∼60 % decrease in NO2 summer mean concentrations within a wide corridor of the ship routes. In addition, the population in coastal areas in northern Germany, Denmark and western Sweden will be less exposed to NO2 in 2040 due to the introduction of the NECA. Due to the lower atmospheric NOx levels, less ozone is formed, and daily maximum O3 concentration over the Baltic Sea in summer 2040 is on average 6 % lower than without the NECA. In the areas close to the main shipping routes, ozone is almost unchanged despite the sharp reduction in NOx emissions, probably due to compensating effects between changed titration losses and changed photochemical ozone production. As expected, levels of atmospheric SO2 are largely unaffected by the NECA (<±2 %).

A secondary effect of the NECA is a reduction in the formation of particulate nitrate. Due to the non-linearity of the atmospheric particle mass formation (i.e. photochemistry and gas-to-particle conversion depend on precursor concentrations and existing particulate matter in a non-linear fashion) the impact of reducing gaseous precursors does not result in a linear reduction in future PM2.5 levels. Figure d shows the change in summer mean PM2.5 concentration pattern due to the NECA. Note that primary emissions of PM2.5 are the same in BAU and NoNECA; thus changes are solely attributed to modified particulate nitrate concentrations. The largest decrease in PM2.5, by up to 8 %, occurs over the Danish islands, where the abundance of ammonium nitrate is highest.

The BAU scenario assumes an improvement in the marine fuel efficiency beyond that required by the EEDI regulation for new ships. With the difference between the EEDI 2040 and BAU 2040 scenarios (land-based emissions are the same in both scenarios), the effect of a slower rate of fuel efficiency improvement compared to the projections in the BAU scenario on the air quality in 2040 is determined. The lower fuel efficiency affects the ship engine emissions and leads to NOx, SO2 and PM2.5 emissions from ships that are on average 37.9 %, 36.8 % and 39.6 % higher in 2040, respectively, compared to the BAU scenario. As a consequence of the lower fuel efficiency, modelled summer mean concentrations of NO2 and SO2 along the main shipping routes in 2040 are higher by 40 % and 25 % than in BAU, respectively (Fig. ).

The lower fuel efficiency has little influence on daily maximum ozone concentrations over the Baltic Sea. Further, the influence of the changed fuel efficiency on atmospheric secondary particle formation is rather limited (not shown). For PM2.5, the higher primary particle emissions compared to BAU do not fully propagate into surface air concentrations (increase by less than 10 %). A large fraction of the ship-related PM2.5 is from secondary formation, which does not increase proportionally with the increased primary PM emissions, for example due to the limited availability of NH3.

Summer-accumulated total nitrogen deposition to seawater in 2040 according to BAU 2040 is below 100 mg(N)m-2 in most parts of the Baltic Sea, with highest deposition remaining in the Belt Sea (Fig. a). The average summer deposition rate for the Baltic Sea is 48 mg(N)m-2. The ship contribution to total nitrogen deposition in summer is massively reduced (by more than 60 %) in the coastal areas of the Baltic Sea region compared to 2012 (Fig. b). Over sea, largest reductions of the ship contribution take place in an area extending from Kattegat to the Arkona basin.

The introduction of the NECA causes a maximum reduction in the summer-accumulated nitrogen over seawater by 18 % compared to not introducing the NECA in 2021 (Fig. c). This means that the Tier 2 fleet in NoNECA 2040 already accomplishes a large reduction in nitrogen deposition compared to today. The effect of the lower fuel efficiency in 2040 (according to EEDI 2040) is an increase in nitrogen deposition compared to BAU, mainly over the northern Baltic Proper and over coastal areas. The relative increase is up to 12 % (Fig. d).

Table  shows the BAU 2040 annual and seasonal nitrogen deposition sums to the entire Baltic Sea seawater surface, for total, oxidized and reduced nitrogen. The ship-related annual nitrogen deposition is reduced by 17.6 ktN, while the overall nitrogen deposition is reduced by 70.3 ktN compared to 2012. Thus the reduction in NOx emissions over the continent, in accordance with a current legislation scenario for land-based emissions in the Baltic Sea region, has a larger impact on the future nitrogen input to the Baltic Sea than the shipping fleet.

In the BAU scenario, with the introduction of the NECA in 2021, NOx emissions from ship traffic in the Baltic Sea are reduced by about 80 % in 2040 because most ships of the Baltic Sea ship fleet will then fulfil the Tier 3 regulation. With the NoNECA scenario, the entire ship fleet follows Tier 2 regulations for NOx in 2040 and, in conjunction with the fuel efficiency increase, leads to an overall NOx emission reduction from the ship fleet by about 50 %.

Table  presents the relative changes in annual mean concentrations of air pollutants in the Baltic Sea region between 2012 and 2040 (as average of the CD04 grid domains). Annual mean NO2 decreases by 61 %–72 % between 2012 and 2040 in the Baltic Sea region, depending on the shipping scenario, with the smallest decrease in the NoNECA scenario.

The BAU scenario adopts the agreed SOx emission abatement regulations: the pre-established SECA limit of 0.1 % S in fuel from 2015 onwards followed by the global limit of 0.5 % S in ship fuels from 2020 onwards. On average, annual mean SO2 decreases by ∼60 % between 2012 and 2040, independent of the shipping scenario. Consequently, particulate sulfate decreases by 50 %–60 % over the Baltic Sea between 2012 and 2040 (not shown) in all three scenarios. The burden of PM2.5 over the Baltic Sea region decreases by 35 %–37 % between 2012 and 2040 (Table ). The reduction in PM2.5 is larger over sea, where it drops by 50 %–60 % along the main shipping routes, and is smaller over the coastal areas. The large drop over sea is due to the reduction in particulate matter emissions from ships and the lower production of sulfate and nitrate related to reduced emissions of primary precursor gases (NOx and SOx) from ship traffic. In most coastal areas the decreased PM2.5 is mainly a consequence of the abatement measures on land.

On an annual average, the daily maximum O3 decreases only slightly over the Baltic Sea region, but the summer average decreases by 10 %–25 %, depending on the shipping scenario, in large parts of Sweden and the Baltic Sea compared to the present day.

Overall, a lower fuel efficiency increase than in BAU has only marginal implications on the future air quality in the Baltic Sea region.

The absolute ship contribution to ambient levels of NO2 and SO2 between 2012 and 2040 changes slightly more than expected due to the reduction in ship emissions. The lower abundance of NOx in the future atmospheric background increases the oxidation capacity of the atmosphere and leads to more efficient oxidation of pollutants via gas-phase reactions and in-cloud processing. Table  presents the relative changes in the annual mean absolute ship contributions in the Baltic Sea region between 2012 and 2040.

A consequence of establishing the NECA is the reduction in the ship contribution to daily maximum ozone by 18 % on average compared to the present situation. If the NECA is not implemented, an increase in the ship-related daily maximum ozone by 31 % results compared to the present day. The introduction of NECA is hence critical for abating ship emissions of NOx to levels that are low enough to sustainably dampen ozone production in the Baltic Sea region. A second important effect of the NECA over the Baltic Sea region is a reduction in secondary formation of particulate nitrate. The introduction of the NECA reduces the ship-related PM2.5 by 72 % in 2040 compared to the present day, while it is reduced by only 48 % without implementation of the NECA.

The effect of the lower fuel efficiency on the absolute ship contribution of air pollutants is limited. Still, the annual mean ship contributions in 2040 to the four pollutants are significantly higher than in the BAU scenario.

A previous study estimated the contribution of airborne nitrogen from international ship traffic to the oxidized nitrogen deposition in the Baltic Sea basin to be about 8 % to 11 % (period: 1997–2006) on an annual average. The contribution from ships with a range from 12 % to 14 % has been reported for the period 2008 to 2011 . In the present study, the relative ship contribution to the deposition of oxidized nitrogen is 24 % (Table ), about twice as high as the previous estimates. However, the total annual nitrogen deposition for 2012 in the present study is 29 % lower compared to the EMEP MSC-W model used by HELCOM . Taking the literature value of 14 % and the oxidized nitrogen deposition flux in 2012 reported by HELCOM (128.9 ktNyr-1; ), an absolute ship contribution of 18 ktNyr-1 is derived, which is only slightly lower than our estimate of 22.5 ktNyr-1.

The relative ship contribution to the total nitrogen deposition is 14 % on annual average and 21 % in summer in the present-day situation (Table ). The ship contribution drops to 5.6 % in 2040 (9 % in summer) when following the BAU scenario (Table ). Between 2040 and 2012 the ship-related deposition of oxidized nitrogen decreased by 78 %. In BAU 2040 the ship contribution to the annual deposition of oxidized nitrogen over the Baltic Sea is only 14 %.

Nitrogen deposition to the seawater of the Baltic Sea decreases on average by 40 %–44 % between 2012 and 2040 (Table ). Depending on the future shipping scenario, the decline in the ship-related nitrogen deposition varies between 46 % and 78 % (Table ). In the EEDI scenario, when the NECA is established but fuel efficiency increase is lower than in BAU, nitrogen deposition in most ship-influenced areas decreases less than in the BAU scenario. The weakest reduction is found for the NoNECA scenario, in which nitrogen deposition decreases by only 30 % over coastal areas of Denmark, Germany and western Finland. The western part of the Baltic Sea would be most affected if the NECA is not implemented (Fig. c).

A linear relationship was found between the emissions of NOx from the Baltic Sea ship fleet and the annual ship-related nitrogen deposition to Baltic Sea seawater (spatial average) based on the results of the present-day simulation and the future scenario simulations (Fig. ). Because the changes in the nitrogen deposition attributed to shipping (Fig. b) between 2012 and 2040 are mainly confined to the Baltic Sea and the surrounding coastal areas, it was expected that the changes in the ship-related deposition flux are proportional to the atmospheric input of oxidized nitrogen via ship emissions. An important link between the ship emissions and the deposition of nitrogen is the formation of HNO3, which constitutes the most important removal pathway for nitrogen in the atmosphere .

The relationship presented above is useful for a quick evaluation of the ship-related nitrogen deposition in future shipping scenarios. Cumulative scenarios based on Shared Socioeconomic Pathways (SSPs) with respect to future ship emission in the Baltic Sea region were designed in the SHEBA project. In scenario SSP3 (regional rivalry), which represents a world with much less international trade and low mitigation capacity , future shipping deviates largely from the predefined regulations but growth of shipping is slower than in BAU by 0.5 % yr-1. The fuel efficiency development is lower by 1 % yr-1 than in EEDI. Use of LNG is similar as in BAU. The Tier 2 regulation is not enforced in SSP3; i.e. the entire ship fleet applies the Tier 1 standard for NOx emissions. Ship NOx emissions in SSP3 are 143 ktNyr-1, somewhat lower than in the current situation. Based on the linear model the ship-related nitrogen deposition is estimated to be 21.5 ktNyr-1.

Thus, in this quick assessment, SSP3 brings a slight improvement in 2040 compared to the current situation. The comparison of the simulated future scenarios to SSP3 also underlines the potential of the Tier 2 standard regulation for newly built ships (as in NoNECA 2040) to reduce the future impact from shipping, compensating, together with the faster fuel efficiency development, the projected higher ship traffic growth.

The ship contribution to air pollutants and nitrogen deposition in the present study was computed using a zero-out method; i.e. the ship emissions were removed in one simulation. An alternative brute-force method would be the perturbation of the emissions, for example reduction by 20 %, which might be more careful with respect to the non-linearity of the involved photochemistry. However, our goal was to derive the impact of shipping in different scenarios, while perturbing emissions is mainly used to investigate short-term responses to expected (small) changes in a sectoral emissions. A previous study by applied the so-called tagging method to assess the ship contribution from each riparian state of the Baltic Sea. Tagging requires adding auxiliary variables to the CTM itself to track pollution. While tagging for inert primary pollutants is straightforward; methods for addressing secondary pollutants require an analysis of the limiting reagents to avoid tagging all possible follow-up products in the gas-phase, aerosol phase and cloud water. Differences between tagging and brute-force methods are usually observed in secondary processes involving precursors from different sources. Some comparison studies (; ) indicate that tagging is advantageous for source allocation rather than for predicting responses to emissions changes.

European regions that are affected by a high density of ship traffic, such as the UK, France, western Germany, North Sea, the southern part of the Baltic Sea and along the ship tracks in the Mediterranean, are currently in a NMVOC-limited regime with respect to ozone formation (Beekmann and Vautard, 2010). In northern Europe, except for the region of the English Channel and parts of the North Sea, a transition from NMVOC-limited to NOx-limited regime is projected until 2020 and the next decades . In a NMVOC-limited regime the production of ozone is sensitive to emissions of NMVOC, while increasing NOx leads to a reduction in ozone by titration. In the NOx-limited regime, ozone is sensitive to emissions of NOx, while it is hardly affected by additional NMVOC emissions.

In the simulations for the future scenarios in 2040, a transition towards a NOx-limited regime most certainly happens in the currently NMVOC-limited areas of the Baltic Sea, in particular along the ship tracks in the southern part. This is clearly seen in the BAU 2040 scenario, where a relative increase in the ship-related daily maximum ozone occurred (due to less titration) over the central shipping routes, whereas the ship-related ozone decreased in the NOx-limited areas outside the ship tracks and over the coastal regions. However, predicted changes in the daily maximum ozone concentrations due to shipping are uncertain because of the lack of data on NMVOC emissions from shipping in the STEAM inventory that were used in the CTM calculations.

We have reduced land-based emissions in the future scenarios in order to obtain a more realistic estimation of the consequences of regulations on shipping emissions on the future air quality in the Baltic Sea region. Based on the model results for the future ship contribution, it is obvious that reduced land-side emissions of primary gaseous precursors amplified the decline in secondary aerosols related to shipping, in particular over the coastal areas. However, the reduction in land-side emissions has a very small effect on the determined ship contributions to NO2 and SO2 over the Baltic Sea (Fig. S15).

The reason for the underestimation of WNO3 and WNH4 in the CMAQ simulations compared to observations of the regional background monitoring stations of the EMEP network, could not be fully resolved. The formation of particulate nitrate involves complex chemistry of several compounds in the gas-phase and multi-component solution systems on aerosols. The simulation of nitrate is highly uncertain because it requires accurate computation of the concentrations of the precursors, e.g. HNO3, NH3, dust and sea salt. The joint underestimation of WNO3 and WNH4 was found in the statistical analysis of model–observation pairs and also in the comparison of modelled and observed seasonal averages. The most convincing explanation at the current stage is that the oxidative conversion of NOx to HNO3 occurs at a too-slow rate in the model, combined with too little particulate ammonium from the regional background that is advected into the Baltic Sea region.

An alternative explanation might be that the wet removal of NO3- and NH4+ in CMAQ is not efficient enough. In addition, the evaluation of simulated precipitation amounts and frequency showed that the southern part of the Baltic Sea receives too little rainfall in summer. For the other seasons and in the northern part the precipitation bias is positive. Too-low precipitation in the southern part, where modelled concentrations of NO3- and NH4+ are much higher compared to the northern part, could be responsible for an average underestimation of the total nitrogen wet deposition to the Baltic Sea.

Coarse-mode particles are removed much faster than fine-mode particles; therefore the deposition of particulate nitrate crucially depends on the uptake to larger particles. Heterogeneous chemical production of nitrate on coarse-mode particles has been found to control the atmospheric nitrate production to a very large extent . The hydrolysis of N2O5 to produce HNO3 is considered in CMAQ by uptake coefficients depending on temperature, RH and particle composition, using the parameterization by , but only for fine-mode aerosols. The Davis parameterization tends to predict too-high N2O5 uptake coefficients near the surface, especially over marine and coastal areas, where relative humidity is high . CMAQ allows for dynamic mass transfer to coarse particles and therefore takes into account the reactive uptake of HNO3 by sea salt particles. Meanwhile, resuspension of mineral dust was not activated in the simulations, and the missing heterogeneous reaction on dust particles surfaces may have contributed to the underestimation of WNO3.