The impact of ship emissions on air quality and human health in the Gothenburg area-Part I: 2012 emissions

The impact of ship emissions on air quality and human health in the Gothenburg area Part I: 2012 emissions Lin Tang, Martin O.P. Ramacher, Jana Moldanová, Volker Matthias, Matthias Karl, Lasse Johansson, Jukka-Pekka Jalkanen, Katarina Yaramenka, Armin Aulinger, Malin Gustafsson IVL, Swedish Environmental Research Institute, P.O. Box 530 21, 40014 Gothenburg, Sweden 5 WSP Environment Sweden, Box 13033, 402 51 Gothenburg, Sweden Chemistry Transport Modelling, Helmholtz-Zentrum Geesthacht, 21502, Geesthacht, Germany Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland

Abstract. Ship emissions in and around ports are of interest for urban air quality management in many harbour cities. We investigated the impact of regional and local ship emissions on urban air quality for 2012-year conditions in the city of 25 Gothenburg, Sweden, the largest cargo port in Scandinavia. In order to assess the effects of ship emissions, a coupled regional and local-scale model system has been set up, using ship emissions in the Baltic Sea and the North Sea, as well as in and around the port of Gothenburg. Ship emissions are calculated with the Ship Traffic Emission Assessment Model (STEAM) model taking into account individual vessel characteristics and vessel activity data. The calculated contributions from local and regional shipping to local air pollution in Gothenburg were found substantial, especially in areas around the city ports. The 30 local shipping contribution of NO2 to annual mean concentrations was up to 3.3 ppb, together with contribution from regional shipping at the North Sea and the Baltic Sea, the contribution was up to 4.3 ppb. In an area close to the city terminals, the contribution of NO2 from local shipping was higher than that of the road traffic, which indicates importance of controlling the local shipping emissions. The local shipping emissions of NOx decreased the summer mean O3 levels in the city by 0.5 ppb on annual mean. The regional shipping lead to a slight increase in the O3 concentrations, however, the overall effect of the regional 35 and the local shipping together was a small decrease of the summer mean O3 concentrations in the city. For PM2.5, the local ship emissions contributed with 0.1 µg m -3 to the annual mean concentrations on the city-domain average, regional shipping was under 2012 conditions a larger contributor to the local PM2.5 than the local shipping, with an annual mean contribution of 0.5 µg m -3 on the city-domain average.

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Based on the modelled local and regional shipping contributions, the health effects of PM2.5, NO2 and ozone were assessed using the ALPHA-RiskPoll (ARP) model. An effect of the shipping-associated PM2.5 exposure in the modelled area was a mean loss of the life expectancy by 0.015 years per person. The relative contribution of the local shipping to the impact of total PM2.5 was 2.2 % which can be compared to 5.3 % contribution from the local road traffic. The relative contribution of the regional shipping was 10.3 %. The mortalities due to the exposure to NO2 associated to shipping were calculated to be 2.6 45 premature deaths/year. The relative contribution of the local and the regional shipping to the total exposure to NO2 in the reference simulation was 14 % and 21 %, respectively. The shipping related ozone exposures were due to the NO titration effect, leading to negative number of premature deaths. Our study show that overall health impacts of regional shipping can be more important than those of local shipping, emphasising that abatement policy options on city-scale air pollution require close cooperation across governance levels. Our findings indicate that the strengthened Sulphur Emission Control Areas 50 (SECA) fuel sulphur limit from 1 % to 0.1 % in 2015, leading to strong decrease in formation of secondary particulate matter on regional scale, has been an important step in improving of the air quality in the city.

Introduction
Shipping is an important source of air pollutants, both on the global and European level (Corbett et al., 1999;Eyring et al., 2005;Cofala et al., 2007). The most important species emitted are sulphur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM) and to some extent carbon monoxide (CO) and volatile organic compounds (VOC). Since nearly 70 % of ship 95 emissions occur within 400 km of coastlines (Corbett et al., 1999), the largest contributions of shipping to air pollution are concentrated to coastal regions with intensive ship traffic and to harbours, where emissions from harbour operations add further to the air pollution generated by ships. The primary air pollutants from shipping contribute to the formation of secondary air pollutants, mainly ozone and secondary particulate matter. On average shipping emissions contributed with 9.4 % to concentrations of primary PM2.5 (particulate matter with a median aerodynamic diameter less than or equal to 2.5 µm) and with 100 12.3 % to concentration of secondary inorganic particulate matter over the Europe during 1997-2003(Andersson et al., 2009. Emissions from the international shipping are controlled through International Maritime Organisation (IMO) and regulations included in the International Convention on the Prevention of Pollution from Ships (MARPOL 73/78) and its annexes. The MARPOL Annex VI-"Regulations for the Prevention of Air Pollution from Ships" sets limits for emissions of SOx and NOx. 105 Sulphur is regulated through maximum allowed sulphur content in the fuel used, while NOx is regulated through Tier limits for maximum specific emissions of NOx from each engine on board. The limits depend on the nominal rotation speed of an engine and different Tiers apply for ships built or substantially re-built in different time periods (2000( -2011( Tier 1, after 2011 Tier 2). For fuel sulphur content (FSC) a global limit of 0.5 % applies since 1 st January 2020, before it was 3.5 %. However, the Baltic Sea, the North Sea and the English Channel are so called Sulphur Emission Control Areas (SECA) where stringer 110 limits apply: in July 2010 it was decreased from 1.5 % to 1.0 %, which is also the limit that applies in this study. In 2015 the fuel sulphur limit was decreased further to 0.10 %. In addition, since 2010 a sulphur content limit of 0.10 % for fuels used by ships at berth for a period longer than 2 hours applied for all EU ports. Sweden has also introduced economic incentives for reduction of the shipping emissions in form of differentiated fairway and port fees with a discount for ships using emission control technologies, contributing to a relatively large share of ships with NOX abatement technology in the region. In 2020 115 the global cap for the FSC will be decreased to 0.50 %. In 2021 a Nitrogen Emission Control Area (NECA) will enter in force in this area with mandatory Tier 3 standard (80 % reduction comparing to Tier 1) for ships built in 2021 and later operating in the region.
In the Baltic Sea and the North Sea, an intensive ship traffic results in high emissions of air pollutants, and contributes to high 120 atmospheric concentrations of particularly of NOx in and around several major ports (Jonson et al., 2015). The relative contribution of shipping in the North Sea and Baltic Sea to coastal NO2 concentrations are highest along the coasts of southern Sweden, the south-western coast of Finland, and the coast of Estonia, 25-40 % on annual average . Jonson et al. (2019) found that the Baltic Sea and North Sea shipping contributed significantly also to concentrations of particulate https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. matter (highest contributions 6-12 %, allocated to similar areas as NOx) and to the deposition of sulphur (highest contributions 125 10-20 %), before the strengthening of SECA fuel sulphur limit. They have also shown that the strengthened limit on the fuel S content in 2015 from 1.0 % to 0.10 % brought a significant decrease in emissions as well as contributions of shipping to air pollution by SO2 and to S deposition (maximum contribution about 2 %) and to a reduction of shipping contribution to the concentrations of PM. Aulinger et al. (2016) and Matthias et al. (2016) studied impacts of the current and future (2030) North Sea shipping on air pollution and found contributions consistent with Jonson et al. (2019) (highest NO2 contributions 25 % 130 and 15 % in summer and winter, respectively, ozone increased by 10% along Scandinavian coast). By 2030, the contribution of shipping to the NO2 and O3 concentrations was estimated to increase by more than 20 % and 5 %, respectively, due to the expected enhanced traffic, if no regulation for further emission reductions is implemented in the North Sea area .

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Several studies have assessed impacts of shipping on human exposure to air pollutants and associated to health impacts. Andersson et al. (2009) assessed impacts of different source regions and also of emissions from international shipping on personal exposure and to the relative increase of death rates from exposure to particulate matter across Europe with help of the atmospheric chemistry-transport model MATCH. They found that shipping, before the introduction of a SECA in the region, contributed with 5 % to population-weighted average concentration (PWC) of primary PM2.5 and with 9 % to PWC of 140 secondary inorganic particles. For individual countries in Northern Europe the contribution to PM exposure varied between 3 % and 19 %. Jonson et al. (2015) assessed health impacts of PM2.5 associated to emissions from ships in the Baltic Sea and the North Sea in years 2009 and 2011, i.e. before and after the SECA FSC limit was strengthened from 1.5 % to 1.0 %, with help of EMEP chemistry-transport model. The relative contributions of shipping to population exposure to PM2.5 were found between 1.6 % and 12 % for 2009, and 1.4 % and 10 % for 2011 for the riparian countries, decreasing by 0-40 % between 145 these years in the different countries. Contributions from shipping to the total exposure to particles in these countries found by Jonson et al. (2015) for year 2009 were by 14-64 % lower than those found in Andersson et al. (2015), accounting that, apart from differences in models and meteorological years used in the 2 studies, Andersson et al. assessed impact of all European shipping prior to SECA regulation entered into force while Jonson et al. assessed impact of the Baltic Sea and the North Sea shipping after the introduction of the 1.5 % SECA fuel sulphur content limit. Barregård et al. (2019) assessed impact of 150 shipping in the Baltic Sea for emission years 2014 and 2016, i.e. before and after strengthening of SECA FSC limit from 1.0 % to 0.1 % using the EMEP model and showed that exposure to PM2.5 associated to the Baltic Sea shipping decreased by 34 % in the region due to this abatement measure, using emissions representative for year 2016, shipping contributed with 10 % to the population exposure of PM2.5 in the coastal regions but only less than 1 % in more remote inland areas.

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The methodologies for calculation of the health impacts of PM2.5 in the above discussed studies vary both in the exposureresponse functions (ERF) used and how the years of life lost are calculated from statistics of mortalities and life-https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.
tables. The most common ERF used is the one recommended by the HARPIE study (WHO, 2013a), increased risk of all-cause mortality 1.0062 (95 % CI 1.004-1.008) per µg/m 3 , which is almost the same as ERF from Poppe et al. (2002). Several studies use a higher ERF presented in Jerret et al. (2005) and in the ESCAPE study (Beelen et al., 2014), both of very similar value, 160 the latter being 1.014 (95 % CI 1.004-1.026) per µg/m 3 . Andersson et al. (2009) calculated increase of death rates from exposure to particulate matter in Europe using ERF from Poppe et al. (2002) for the secondary inorganic aerosol and ERF from Jerret et al. (2005) for the primary PM2.5, reasoning that the ERF of Jerret et al., based on intra-city gradients is better representing the impact of primary PM2.5, while Poppe et al. uses the inter-city differences reflecting more impact of secondary PM. Combining the increase of mortality from particulate matter in EU27 and the relative contribution of shipping to the 165 exposure to primary and secondary inorganic PM, Andersson et al. (2015) found the resulting impact of shipping on mortality and Beelen et al. (2014), respectively. In our study the impacts of exposure to shipping-related air pollutants on health of people living in the Gothenburg region have been assessed using the ALPHA-RiskPoll methodology (ARP, Holland et al. 175 2013Holland et al. 175 , Åström et al., 2018 which uses the ERFs from the HARPIE project (WHO, 2013a).
The city of Gothenburg is located on the western coast of Sweden, with about 0.57 million inhabitants and an area of 450 km 2 .
The dominant wind direction in Gothenburg is south-west with average wind speed of 3.5 m s -1 , indicating the major transport path from sea to the land, especially in summer. The geomorphology of the Gothenburg area is described as a fissure valley 180 landscape dominated by a few large valleys in north-south and east-west directions. The major air pollution sources in Gothenburg are above all road traffic and industry, wood burning, shipping, agriculture, working machines and long-range transport (LRT) from the European continent and other parts of Sweden. The harbour and shipping activities are important emission sources and directly influences the urban air quality. The centre of the city is situated on the southern shore of the river Göta älv. The Port of Gothenburg receives between 6,000 and 6,500 calls per year and additional 600-700 ships pass to 185 and from ports upstream and on the Göta älv. The port annually handles approximately 900,000 containers, 20 million tonnes of petroleum, and half a million Roll-on/roll-off (RoRo) units (Winnes et al., 2015). Passenger traffic in Gothenburg is also very busy with 1.5 million passengers who ferry to and from Gothenburg to Denmark, Germany etc. on Stena Line ferries each year. This makes the port the largest cargo port in Scandinavia.
Comparing with other European cities, the air pollution levels in Gothenburg are low and the air quality has become better and better since the 70s because of the effective emission control addressing industry and road traffic. The trends of SO2, NOx and NO2 have been continuously decreasing from 1990 to 2015, except for the areas close to major roads (Miljöförvaltningen, 2017). O3 exhibits an increasing trend and there is also a slowly increasing trend for PM10 in Gothenburg (Olstrup et al., 2018).
The annual means for NO2, PM10 and PM2.5 (particulate matter with aerodynamic diameter less than or equal to 10 µm and 2.5 195 µm, respectively) during the period 2000-2017 are 12.5 ppb, 16.3 µg/m 3 and 7.9 µg/m 3 , respectively, at an urban background site in Gothenburg. The decreased levels of NOx and NO2 during the period 1990-2015 in Gothenburg were estimated to increase the life expectancy by up to 12 months and 6 months respectively, and the slight increased trend of O3 and PM10 have relatively little impact on life expectancy (-2 month and -1 month) (Olstrup et al., 2018). In terms of exposure to PM10 and PM2.5 from different source categories in Gothenburg, Segersson et al. (2017) calculated that the largest part was due to the 200 long-range transport and the dominating local sources were road traffic and residential wood combustion, while the contribution from local shipping was small, 0.04 µg/m 3 population weighted annual mean PM2.5. The exposure of PM2.5 from shipping in other harbour cities in Sweden are lower than in Gothenburg, with 0.02 µg/m 3 in Stockholm and 0.01 µg/ m 3 in Umea (Segersson et al., 2017).

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This study has been conducted within the BONUS SHEBA project (Shipping and Environment of the Baltic Sea Region) where the impact of current and scenario emissions from ships on air quality have been investigated as a part of a holistic assessment framework for impacts of shipping on marine and coastal environment. The shipping-related air pollution has been investigated on a range of spatial scales with several chemistry-transport models: coarse spatial scale resolution was used for simulations in the European domain, finer resolution was used for the Baltic Sea (Karl et al., 2019a, c), and city-scale 210 simulations using high spatial resolution were used for several harbour cities (Ramacher et al., 2019a). The present study (Part I) evaluates the contributions of regional and local shipping to the concentrations of SO2, NO2, PM2.5, O3 and secondary PM, as well as the human exposure and the associated health impacts in Gothenburg for year 2012. Health impact studies for the shipping emissions in cities are rare, mainly because the spatial resolution of the regional CTM (Chemical Transport Model) does not allow for the city-scale resolution. This study provides the city-scale Health Impact Assessment (HIA) and identifies 215 and addresses potential health impacts associated to local and regional shipping. The studied year (2012) has been considered as a present-day "normal year" for Baltic Sea Region in terms of meteorological conditions in BONUS-SHEBA. In terms of ship emission regulations, the study presents a situation with 3.5 % FSC global limit, 1.0 % FSC limit in the SECA area whereas 0.1 % FSC limit applies for ships berthing in the port of Gothenburg or operating within the Göta älv estuary. Several alternative shipping scenarios in year 2040 are discussed further in Ramacher et al., 2019b (Part II). 220 https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.

Model set-up
For the city-scale chemistry transport model (CTM), the prognostic meteorology-dispersion model TAPM (The Air Pollution Model) (Hurley et al., 2005;Hurley, 2008)   The chemical boundary conditions were taken from the Community Multi-scale Air Quality Modelling System (CMAQ) (Byun and Ching, 1999;Byun and Schere, 2006). CMAQ model simulations on a 4 km × 4 km grid (Karl et al., 2019c), which were used for the chemical boundary conditions, were driven by the high-resolution meteorology meteorological fields of the 245 COSMO-CLM (Rockel et al., 2008) version 5.0 using the ERA-Interim re-analysis as forcing data. Chemical boundary conditions for the CMAQ model simulations were provided through hemispheric CTM simulations, from a SILAM model (Sofiev et al., 2006) run on a 0.5˚ × 0.5˚ grid resolution, which was provided by Finnish Meteorological Institute (FMI). Land based emissions for the regional-scale simulations were represented by hourly gridded emissions calculated with SMOKE-EU emission model (Bieser et al., 2011). The SMOKE-EU emission data is based on reported annual total emissions from the 250 European point source emission register (EPER), the official EMEP (www.ceip.at) emission inventory and the EDGAR HTAP v2 database (EPER, 2018;CEIP, 2018;Olivier et al., 1999). For shipping emissions, the model used an inventory calculated with the STEAM model consistent with the inventory used by TAPM for the city-scale, calculated with 2 km × 2 km grid https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. resolution (STEAM3, Johansson et al., 2017), more details are given in the next section. The STEAM model version used for the CMAQ simulations was, however, not including VOC emissions. As chemical boundary conditions, vertical model layer 255 seven with a mid-layer height of approximately 385 m above ground was selected. CMAQ simulations with and without ship emissions in the Baltic Sea and the North Sea included were used in TAPM simulation runs. Since the TAPM allows just 1-d boundary concentration fields with hourly time resolution, the TAPM boundary concentrations were calculated using the horizontal wind components on each of the four lateral boundaries for weighting the upwind boundary concentrations around the TAPM model domain (Fridell et al., 2014). The city-scale model set-up are summarised in Table 1. 260

Road traffic emissions
The road traffic emissions were calculated from traffic activity data and emission factors. The basic set of emission factors from road vehicles was extracted from HBEFA v. 3.2 (HandBook Emission FActors for Road Transport, Rexeis et al., 2013). HBEFA comprehends emission factors for different classes of road vehicles based on type of vehicle (e.g. motorcycles, light-280 duty, heavy-duty vehicles), technology or fuel (e.g. petrol, diesel, hybrid) and emission standard (pre-Euro-Euro 6). For each of those also a number of road categories and driving patterns that affect emissions are specified within the vehicle subsegments. The emission factors for light-duty and heavy-duty vehicles and busses in Gothenburg were calculated using the Swedish national database on car fleet composition and national, vehicle-type specific, activity data in 2012. Road traffic emissions were finally calculated using traffic activity data for Gothenburg (vehicle kilometres for light duty vehicles plus 285 motorcycles, heavy duty vehicles and busses on road links with specified type, speed and congestion hours) from the database https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. of the Environmental Administration, City of Gothenburg (Miljöförvatningen), and corresponding emission factors calculated in the HBEFA database. These data were applied as line emission sources in the model.

Other emissions 290
Ten large point sources from industrial processes are present in the city-scale model domain, these are all fugitive emissions from fuel handling and refineries. For technical reasons these were considered as area sources in the model with release heights corresponding to the stack heights allocated to these sources. The emission factors from these industrial sources were obtained from Swedish Environmental Emission Data (SMED 2015) for 2012.

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Emissions from the following sectors were geographically distributed on 1 km × 1 km grid and assigned with coordinates and emission height: 'Manufacture of solid fuels and other energy industries', 'Combustion in industry for energy purposes', 'Stationary combustion in agriculture/forestry/fisheries', 'Energy and heat production (commercial/institutional)', 'Residential plants (boilers), domestic heating, working and off-road machinery', 'Use of paints and chemical products in households and enterprises', 'Agriculture, waste and sewage', as well as 'Other transports' (the landing and take-off emissions from aviation, 300 trains and military). They stem also from the SMED database and were obtained from SMHI (Swedish Meteorological and Hydrological Institute). These emissions were applied as gridded sources in the model.

Design of model simulations
Several model simulations were performed to investigate the influence of shipping within the city domain and influence of the regional shipping outside the city on air pollution in 2012: (1) A simulation including complete emission inventory both in the city-scale simulation and in the CMAQ simulation supplying the chemical boundary conditions: "Base scenario"; 315 (2) A simulation excluding the local shipping in the TAPM city domain but including regional shipping chemical boundary conditions in the CMAQ simulation: "No local shipping scenario" and; (3) A simulation excluding both local shipping in the TAPM city domain and shipping in the chemical boundary conditions in the CMAQ simulation: "No local and regional shipping scenario". https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.

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In addition, three sensitivity studies were performed within this study: (1). "No NMVOC from local shipping" had the same emission input as the "Base scenario", but without NMVOC from local shipping emissions. The difference between "Base" and "No NMVOC from local shipping" was used to investigate the impact of VOC emissions from local shipping, which is often neglected in the emission inventories due to its small proportion and as these has only recently been included in the STEAM model; 325 (2). "Primary PM from local shipping" had the same emission input as the "Base scenario", but for the local shipping only the primary PM emissions as calculated by the STEAM model were included, all emissions of the gaseous species were excluded preventing formation of the secondary PM from local shipping. The difference between "Base scenario" and "Primary PM from local shipping" reflects the formation of secondary PM from SO2, NOx and VOC emitted by the local shipping; 330 (3). "No road traffic" had the same emissions as the "Base scenario", but without road traffic emissions. It was used to compare the contributions of shipping emissions as well as the health impact of shipping with emissions from the city traffic.

Model evaluation
Model evaluations were carried out for both meteorological and air pollution parameters. The simulated meteorological 335 parameters (temperature, relative humidity, wind fields and precipitation) were evaluated with measurements at four stations: Moreover, a performance criteria can be calculated, which is combining the statistical performance indicators with fixed parameters to evaluate whether the model results have reached a sufficient level of quality for a given policy support application 355 (Pernigotti et al. 2014). According to the DELTA tool, the capability of a model to reproduce measured concentrations is good when more than 90 % of the stations fulfil the performance criteria. We applied the Delta Tool to concentrations of NO2, PM10 and O3 measured at the available urban background sites and road traffic sites, compared them with concentrations calculated by our model system and calculated both, statistical performance indicators and the model performance criteria.

Health impact assessment 360
The health impacts of exposure of population in Gothenburg to shipping-related air pollutants were assessed with the ALPHA-RiskPoll (ARP) methodology (Holland et al., 2013) providing for calculation of a wide range of air-pollutant specific health effects based on the population weighted concentrations, national population statistics on age distribution of population, mortality and morbidity data and effect-specific exposure-response relationships. The methodology has been developed and To calculate the health risks, further information needed is the ERF and the baseline health statistics including the life expectancies, the death rates and morbidity data for estimating the impacts on mortality and morbidity. To estimate YOLLs, the age at which the premature deaths occurred should also be considered. In the ARP model, the ERFs used are those from WHO (2013a): 6.2% (95% confidence interval 4.0-8.3%) relative risk increase per 10 µg/m 3 increased exposure for the PM2.5 390 exposure, 0.29% (95% confidence interval 0.14-0.43%) relative risk increase per 10 µg/m 3 increased exposure for the ozone exposure and 0.27% (95% confidence interval 0.16-0.38%) relative risk increase per 10 µg/m 3 increased exposure for the NO2 exposure. The YOLLs are calculated per year. The analysis was made separately for the population exposure related to the different pollutants from local and regional shipping.

Model evaluation
The model evaluation was conducted for both meteorology and air pollution in the inner-most model domain. The comparison between measured and modelled local meteorological parameters (temperature, relative humidity, total solar radiation, wind speed, wind direction and precipitation) shows high correlation and low bias. The application of ERA5 datasets in the model shows significant improvements from the default reanalysis datasets. Nevertheless, the predictions of the meteorological 400 parameters such as wind fields flow get better with wind field assimilation, for more detail see Ramacher (2018). For example, the differences between observed and simulated wind rose at Femman in January and July indicate a good model capability to reproduce local wind field except for missing about 30% of low wind speeds (0-2.5 m s -1 ) from the north (Fig. 4), which may introduce some underestimations in high pollutant concentrations at ground due to accumulation in the boundary layer.
Nevertheless, the total frequency of northerly winds at Femman station is low in January (8-17%) and very low in July (1-405 8%).
The evaluation of ambient pollutants was conducted through the major statistical parameters. At urban background site, the estimation of NO2 and PM10 concentrations were satisfactory in summer with lower bias, however the model tended to underestimate NO2 and PM10 concentrations in winter. O3 evaluation was carried out at station Femman and Möldal, and 410 underestimation of daily maximum of the 8-hour means was also detected, which could be caused by low resolution of local NO sources and hence more smoothed titration of ozone. The summary statistics according to the FAIRMODE model evaluation tool shows that less than 90% of daily PM10 concentrations at road site Haga fulfill the performance criteria for the statistic indicator Hperc (Fig. 5). The indicator Hperc indicates the model capability to reproduce extreme events, represented by selected high percentile for modelled and observed values. 415 https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.
The underestimation of NO2 and PM10 especially at road sites demonstrate impact of too coarse spatial resolution (250 m × 250 m) not capturing high concentrations at street level, possible missing or insufficient cover of local emissions like resuspension particulate matters from traffic sources, or incomplete chemical reactions in the model etc. As pointed out by Karl (2019b), recent nested model approaches have not resolved the details in the emission processing and near-field dispersion 420 at the street and neighborhood level. However, shipping emissions are, when reaching the exposed population, more dispersed and the 250 × 250 m 2 grid resolution should be sufficient to assess their impact. Nevertheless, the other statistic indicators (Mean, exceedances, Normalized Mean Bias, Normalized Mean Standard Deviation, Correlation coefficient, etc.) of model performance in Fig. 5 show a satisfactory performance of the used city-scale model for Gothenburg.

SO2
The modelled annual average SO2 concentrations from all sources, including local and regional shipping are shown in Fig. 6.
The calculated annual mean concentration of SO2 from all sources in the model domain is 0.4 ppb and local shipping contributes 0.05 ppb on model-domain average and up to 0.6 ppb in a wide area around the main shipping routes and ports.
An additional increase of 0.1 ppb SO2 is detected when considering the regional ship emissions. In summer months (JJA), local 430 and regional shipping contribute on model-domain average with 0.1 ppb to SO2 concentrations and with 0.8 ppb in maximum ( Fig. S1 in the Supplement). The highest SO2 contributions (maximum 0.6 ppb on annual mean) were found around the major ports: Älvsborgshamnen, Skandiahamnen, Skarvikshamnen, Ryahamnen, Lindholmshamnen, and Frihamnen in the northern bank of the Göta älv (Fig. 6d). In addition, two busy ferry terminals located on the southern bank of the Göta älv can contribute to the high SO2 concentrations on the opposite river side due to the dominant south-westerly winds. 435

NO2
NOx is mainly emitted as nitrogen oxide (NO), in the STEAM model the NO2/NOx ratio is 5%. In atmosphere NO is quickly converted to NO2 in reaction with ozone, so further from the source the atmospheric NOx is dominated by NO2, approaching a photo-stationary state driven by the NO+O3 reaction and NO2 photolysis. Maps of modelled annual mean atmospheric concentrations of NO2 over the Gothenburg area are shown in Fig. 7. The annual mean concentration of NO2 in the Base 440 simulation is 3.7 ppb as the model-domain average (Fig. 7a), and the model-domain mean contribution from local shipping to the annual mean concentrations is 0.5 ppb and up to 3.3 ppb in areas with high contribution (Fig. 7b). The calculated modeldomain mean contribution to NO2 concentrations from local and regional shipping together is 1.5 ppb (41%) and up to 4.3 ppb in most heavily impacted areas (Fig. 7c). The seasonal differences in NO2 concentrations are driven by emissions, atmospheric chemistry and atmospheric mixing. Maps of modelled air concentrations of NO2 over the Gothenburg area in winter and 445 summer month are shown in Fig. S2 in the Supplement. The higher contribution of local shipping in summer and larger influenced areas is due to 20% higher summer emissions comparing to winter, different photochemical state as well as different https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. local meteorological conditions. The dominated south-westerly winds in summer transport NO2 from the shipping routes and port areas farther inlands. Again, the highest level of NO2 is around the port Skandiahamnen.

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Eriksberg is a modern residential and commercial center built in place of an old dockyard area. The daily influence from local and regional shipping is obvious and illustrated by the calculated daily mean NO2 concentrations from the three model simulations "Base", "No local shipping" and "No local and regional shipping" (Fig. 8). The local shipping contributes with 2.5 ppb to daily mean concentrations of NO2 on average, and up to 11.1 ppb in March. Meanwhile the regional shipping contributes with 1.0 ppb to daily mean NO2 concentrations on average and reaches 9.2 ppb at most. For comparison, the daily 455 average contributions of NO2 from road traffic are also presented in Fig. 8. In total, 219 days in 2012 show contributions to daily mean concentrations from local shipping higher than those from road traffic at Eriksberg. Even though road traffic is the major contributor to the NO2 concentrations in urban environment, the local ship emissions should not be neglected, especially in areas close to the city ports.

O3 460
O3 is formed in photocatalytic cycles involving NOx, ozone and hydrocarbons, through the photolysis of NO2 in sunlight. The same cycle involves also titration of ozone by the reaction with NO forming the NO2. Maps of modelled atmospheric concentrations of ozone over the Gothenburg area in 2012 are shown in Fig. 9, with focus on summer months (JJA). The regional background concentration of ozone at a regional background stations close to Gothenburg area is 37 ppb in the summer of 2012. Modelled summer ozone levels in the model domain are in the 15-30 ppb range (Fig. 9a). Since NOx is mainly emitted 465 as NO, the emissions from local shipping cause local reduction of ozone concentrations due to the titration of O3 by NO in the main shipping routes and port areas (Fig. 9b). The O3 depletion pattern along the north of the Göta älv is 4 ppb in maximum due to the local shipping with both NOx and NMVOC emissions (Fig. 9b), while regional shipping emissions tend to increase the ozone concentrations by 1 ppb over the land compared to 4-6 ppb over the remote ocean due to large-scale ozone production in summer (Huszar et al., 2010, Fig. 9c). 470 In the local STEAM inventory, the non-methane volatile organic compounds (NMVOC) from shipping are also available.
These NMVOC serve as precursors of O3 and enhance photochemical ozone production. TAPM model uses concept of VOC reactivity instead of individual NMVOCs, producing pool of peroxy radicals which take part in the ozone-production photocatalytic cycle. A sensitivity run was performed to study the impact of NMVOC emissions from local ships on ozone 475 concentrations in the city by excluding the local shipping NMVOC emissions from the simulation. Fig. 9d shows the impact of the NMVOCs emissions: the O3 concentrations increased by up to 2 ppb along the main shipping routes and the port areas, which means the negative effects of NOx emissions from local shipping on the ozone concentrations was 6 ppb in maximum when NMHC emissions were excluded comparing to 4 ppb in the Base simulation.

Particulate matter 480
Particulate matter includes primary, directly emitted particles and secondary particulate matter formed upon further processing of emissions in the atmosphere. At the urban background site Femman, close to the city harbour, the measured annual mean PM2.5 concentration was 7.9 µg m -3 in 2012. The calculated annual mean PM2.5 in the Base simulation was 4 µg m -3 as the model domain average (Fig. 10a). The local ship emissions contributed with 0.1 µg m -3 (3 %) to the annual mean as the modeldomain average (Fig. 10b). Regional shipping was under 2012 conditions a larger contributor to the local PM2.5 than the local 485 shipping with annual mean average contribution of 0.4 µg m -3 (11 %) (Fig. 10c). In summer, the area with major influence of shipping emissions extended to the north of the Göta älv with maximum contributions from local plus regional shipping of 1.4 µg m -3 at Skandiahamnen (A3 in Appendix). At the near-harbour residential area Eriksberg, the contribution from the local and regional shipping was in range 0.2-1.1 µg m -3 on monthly mean, representing about 5-29 % contribution to the calculated monthly mean PM2.5 concentrations and contribution from the regional shipping dominated in the months from winter to 490 summer (Fig. 11).
In the chemistry mode of TAPM, simplified chemical reactions for the secondary PM are included and the secondary particulate matter consists of organic carbon, reactive nitrogen and sulfate. While in summer more intensive photochemistry favors formation of precursors to the secondary PM, the air temperature and humidity controlled gas/particle partitioning of 495 ammonium nitrate causes higher PM-nitrate concentrations during periods of the year with cold and wet weather. Many cityscale models do not involve chemistry and thus neglect formation of the secondary PM. Therefore, a sensitivity run was performed to investigate the role of the formation of secondary PM from local shipping on the city scale where only emissions of the primary PM were introduced, without emissions of the gas-phase pollutants from the local shipping. Modelled secondary PM concentrations from shipping were calculated as the difference between the base run and this sensitivity run. They were 500 found relatively low, with maximum 0.0009 µg m -3 in winter, and spread out since secondary PM is mainly formed far from the sources. The secondary PM tends to disperse and accumulate to the east part of Gothenburg due to the prevailing wind directions (Fig. A4 in the Appendix).

Calculation of exposure and health effect from ship emissions
The contribution of emission sources to population exposure depends on the relationship between population density and air 505 pollution levels. The areas with relatively high exposure to PM2.5 due to local and regional shipping are city ports and areas around, especially north of the Göta älv. Figure 12 presents the population weighted annual mean concentrations of NO2, PM2.5 and SOMO35 at each model grid for the base simulation and for contributions of the local plus regional shipping, as well as for contributions of the road traffic. The spatial patterns of PM2.5 exposure from shipping are dominated by gradients in the concentration fields around the city ports to the north of Göta älv. PM2.5 exposure from shipping is higher than exposure from 510 road traffic in a larger city area since regional-shipping-related PM2.5 exposure is evenly distributed over the city (Fig. A5 in https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. Appendix). The sum of PWC of PM2.5 from the local plus regional shipping is 0.51 µg m-3 in the model domain, to which the regional shipping contributes with 82 %, comparing to 0.22 µg m-3 associated to road traffic ( Table 2). The total exposure to PM2.5 is dominated by particles transported to the city with the background air. The sum of PWC of NO2 from regional and local shipping was 1.65 ppb, similar to that from the road traffic (1.75 ppb), with gradients in the concentration fields north of 515 the Göta älv. Because of the effect of local O3 titration by the shipping emitted NO, the exposure to SOMO35 from shipping was negative along the Göta älv. However, SOMO35 exposure due to regional shipping was positive with sum of PWC 70.9 ppb×h in the model domain and showed relatively high level in areas with high population density.
The PWC for these pollutants were then used in the health impact calculations and results are presented as life years lost per 520 year and loss of life expectancy (years of lifetime lost per person, YOLLs pers. -1 ) for PM2.5 and as premature deaths for ozone and NO2. The estimated loss of life expectancy (YOLLs pers. -1 ) due to PM2.5 from local shipping was 0.003 while from the regional shipping it was 0.014. For comparison, impact of exposure to PM2.5 from the road traffic was calculated to be 0.007 YOLLs pers. -1 and to all PM2.5 in the Base simulation to be 0.14 YOLLs pers. -1 (Table 3). In all, shipping contributed with 12 % to the calculated health impacts from the total exposure to PM2.5 in the city and the impact was more than 2 times larger 525 than that of the local road traffic, the regional shipping being a larger risk for human health than the local shipping (> 80 %) in Gothenburg. The exposure to ozone related to shipping emissions reduced the acute mortality by 0.4 premature deaths per year due to the NO titration effect. This effect included additional 0.03 deaths attributed to ozone formed from the regional shipping emissions (Table 3). Exposure to NO2 related to shipping emissions caused additional 2.6 premature deaths year -1 , impact of the local shipping being similar to the regional one. This impact corresponded to 35 % of the impact of the NO2 530 exposure in the Base simulation and was similar to the impact of the road traffic.

Assessment of uncertainties and comparison with other studies
Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health due to the complex associations between environmental exposures and health. Uncertainties are introduced with 535 the calculated pollutant concentrations, the grid resolution when assessing the population exposure, the general shape of concentration-response function and transferability problems of the function from region to region. Hammingh et al. (2012) presented an estimate of the uncertainty in the calculations of YOLLs, which may stem from the methodology used in the The largest uncertainties are associated with the exposure response functions (ERF) as such. In this study impacts for the mean 545 values of ERFs are presented, the 95 % confidence interval for these functions is given in the Methods chapter. The ERFs used here are those recommended in WHO (2013a), for PM2.5 ERFs with higher values for spatial analyses of air pollution and mortality were found by project Escape for European cohorts (Beelen et al., 2014) as well as for mortalities in Los Angeles (Jerrett et al., 2005, 17 % per 10 µg m-3, 95 % confidence interval 5-30 %). These ERFs are of very similar value and those of Beelen et al. (2014) were used as alternative functions for estimates of broader uncertainty limits by Barregård et al. (2019). 550 There are two important issues regarding the uncertainties associated with the ERFs. First, the air pollution represents a complex mixture and individual gases and particles are often correlated. The impacts on mortality calculated for the different pollutants therefore cannot be simply summed up. Second, the ERFs assume that all particulate matter has the same impact.
There is increasing evidence of different ERFs for some compounds, primarily elemental and organic carbon (WHO 2013b).

555
The most robust relation between the air pollution and effects on human health is for particulate matter (WHO 2013b). In Swedish cities, also in Gothenburg, the main contribution to concentrations of PM2.5 comes from the background air (Segersson et al., 2017;Gustafsson et al. 2018). Accurate modelling of the total concentration of particulate matter is, however, very difficult as the processes affecting them are extremely complex and many of them not well quantified. These include natural and anthropogenic emissions, formation of secondary particulate matter in complex photochemical processes as well as dry 560 and wet deposition processes that need to be described on the whole range of relevant geographical and time scales. Many regional-and global-scale models tend to underestimate the simulated PM2.5 concentrations, especially in summer, when formation of secondary PM is stronger due to the high photochemical activity and the impact of primary PM is lower due to the more intensive mixing and smaller anthropogenic emissions of primary PM in summer (Karl et al., 2019a). Also, the modelled PM concentrations used as the boundary conditions in this study showed underestimates of PM2.5 by 60 % and 17 565 %, on summer average and on annual average, respectively (Karl et al., 2019a). Two studies addressing impacts of shipping on air pollution in Gothenburg (Segersson et al., 2017;Repka et al., 2019)  in this study were lower than in Segersson et al. (2017) and Repka et al. (2019), but they agree reasonably well with Jonson et https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License. al., 2015. Segersson et al. (2017 addressed health effects of PM2.5, PM10 and black carbon in three Swedish cities, among them Gothenburg using gaussian model SIMAIR. The population weighted exposure to PM2.5 for Gothenburg was 580 calculated to 6.5 µg m -3 , which was associated with c.a. 150-290 premature deaths from exposure to PM2.5. The lower premature death number in Segersson et al. (2017) comes from calculations using the same ERF as in this study while the higher number uses the ERF presented by Jerrett et al. (2005)  It is important to bear the uncertainties in total concentrations of PM and other air pollutants in mind when assessing the relative contribution of shipping to the overall impact of air pollution. Assessments of impacts of selected anthropogenic 590 sources are, however, associated with smaller uncertainties compared to the impact of the total concentrations as some large uncertainties, e.g. those regarding the natural and agriculture sources, cancel out. The study of Segersson et al. (2017) found contribution of shipping to the population weighted annual PM2.5 concentration to be 0.04 µg m -3 and contribution of the road traffic exhaust emissions to be 0.27 µg m -3 which can be compared to 0.09 µg m -3 from shipping and 0.22 µg m -3 from road traffic found in this study, however, bearing in mind that the studies assessed two different years. 595

Conclusions
The impact of local and regional ship emissions on in the city of Gothenburg was investigated by a multi-model system for the year 2012. The model evaluation against monitoring data demonstrated fairly good agreement in meteorological parameters and acceptable estimation of hourly air pollutant concentrations.

600
The city-scale model simulations with and without local and regional shipping in the emission inventory revealed that impacts from shipping on air quality in Gothenburg were substantial. The calculated contribution from local shipping to NO2 was 0.5 ppb on annual average, representing 14 % of calculated annual mean NO2 concentration. Including contribution from regional shipping in the North Sea and the Baltic Sea, the total shipping contribution reached 1.5 ppb representing 41 % of calculated NO2 concentrations. The contribution from regional and local shipping was higher than that from road traffic around the area 605 of the city ports. In an analysis of exposure from different sources using population weighted concentrations, the contribution of regional and local shipping was similar to that of the road traffic in the city.
The model results of ozone concentrations have shown that titration by NO dominated the overall impact of local shipping on ozone concentration levels in Gothenburg. The maximum impact from local NOx and NMVOC emissions on summer seasonal 610 mean ozone concentration was calculated to -4 ppb. The negative effect of solely NOx emissions from local shipping on the ozone concentrations was up to -6 ppb, net negative even in the summer, when photochemical activity and potential for ozone formation are high. The emissions of NMVOC from local shipping as such increased the ozone formation in the city with the highest contribution of 2 ppb as seasonal summer mean. In terms of urban air quality control, reduction in anthropogenic NMVOC could result in a significantly greater decrease in O3 relative to the same reduction in NOx (Karl, 2019b). 615 The simulated emissions from local and regional shipping contributed 0.5 µg m -3 on model/domain average and at highest 1.1 µg m -3 to the annual mean concentration of PM2.5. Regional shipping is a larger contributor than local shipping to local PM2.5 concentrations, corresponding to 11 % of the local PM2.5 concentrations on average. Also its contribution to the PWC was higher, contributing with 0.4 µg m -3 (10 % of the total PWC for PM2.5). Contribution from the local shipping was 0.1 µg m -3 620 (2 % of the total). The calculated health impacts have shown the most serious effects from shipping in Gothenburg to be associated with exposure to PM2.5. Local and regional shipping together reduce life expectancy by 0.015 years per person, of which more than 80 % are associated with the regional shipping in the North and the Baltic Sea. The shipping impact is more than twice as high as the 625 modelled impact of PM2.5 associated with the local road traffic. Impacts from exposure to NO2 and ozone were calculated in terms of premature deaths per year and 2.6 additional cases year -1 were calculated for exposure to NO2, regional and local shipping contributing with 59 % and 41 % respectively. Impacts from exposure to ozone were of opposite magnitude. The decrease of ozone due to the NO titration reduced the calculated mortalities by 0.4 cases year -1 . The impact of the exposure to PM2.5 from shipping calculated as premature deaths was 18 cases year -1 . The implementation of the more stringent SECA 630 regulations on FSC in year 2015 is not likely to have changed impacts from NO2 and ozone. According to the study of Jonson et al. (2019), approximately 35 % reduction of the impact from the regional shipping contribution to PM2.5 could be expected around Gothenburg while a much smaller change can be expected in emission from the local shipping since hotelling and inland shipping already use a fuel with 0.1 % FSC in the model. This would mean similar reductions of the impacts related to PM2.5 in the city of Gothenburg. Impact of the global cap of 0.5 % for FSC which entered into force the 1 st of January 2020 635 will not have any significant impact on further reduction of shipping-related air pollution in Gothenburg comparing to situation after 2015. Global study of Sofiev at al. (2018) shows that around the Swedish West coast decrease of PM2.5 due to the global cap would be below 1%. The more serious health effects induced by regional shipping indicate that close cooperation across governance levels is required to effectively reduce the air pollution in the city. Impacts of the local shipping emissions on air quality and human health are further discussed in part II paper (Ramacher et al., 2019, the Part II paper), presenting study of several future shipping scenarios for year 2040 adopting changes in shipping emissions due to changes in ship traffic volumes, legislation on emissions of air pollutants at sea, on energy effectivization as well as introduction of shore side electricity in shipping.

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This article is part of the special issue "Shipping and the Environment 2017"

Code availability
The TAPM model is a commercial software available at CSIRO, Australia (www.csiro.au). STEAM model is intellectual property of the Finnish Meteorological Institute and is not publicly available. ARP is commercial software available from arirabl.com. 650

Data availability
The model output data are available upon request from the corresponding authors      at the north of the Göta älv for the year 2012. Modelled local shipping contributions (black line) deduced from the scenarios "Base" and "No local shipping", regional shipping contributions (red line) deduced from the scenario "No local shipping" and "No local and no regional shipping", and road traffic contributions (blue line) deduced from the scenario "Base" and "No road traffic" are presented.
https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.  https://doi.org/10.5194/acp-2020-94 Preprint. Discussion started: 18 February 2020 c Author(s) 2020. CC BY 4.0 License.   Table 3: Health impacts calculated for O3, NO2 and PM2.5 contributions of the local and regional shipping and the local road traffic to air pollution in the city of Gothenburg as well as of the total exposure to these pollutants in the city. The health impacts calculated with the ARP model and with the RAINS methodology are presented.