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 conditions in the city of 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 were calculated with the Ship Traffic
Emission Assessment Model (STEAM), 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 to be substantial, especially in areas around the city
ports. The relative contribution from local shipping to annual mean NO2 concentrations was 14 % as the model domain average, while the
relative contribution from regional shipping in the North Sea and the Baltic
Sea was 26 %. In an area close to the city terminals, the contribution of
NO2 from local shipping (33 %) was higher than that of road
traffic (28 %), which indicates the importance of controlling local
shipping emissions. Local shipping emissions of NOx led to
a decrease in the summer mean O3 levels in the city by 0.5 ppb
(∼2 %) on average. Regional shipping led to a slight
increase in O3 concentrations; however, the overall effect of
regional and the local shipping together was a small decrease in the summer
mean O3 concentrations in the city. In addition, volatile organic compound (VOC) emissions from
local shipping compensate up to 4 ppb of the decrease in summer O3
concentrations due to the NO titration effect. For particulate matter with a median
aerodynamic diameter less than or equal to 2.5 µm (PM2.5), local
ship emissions contributed only 3 % to the annual mean in the model domain,
while regional shipping under 2012 conditions was a larger contributor, with
an annual mean contribution of 11 % of the city domain average.
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 decrease in the life expectancy by
0.015 years per person. The relative contribution of local shipping to
the impact of total PM2.5 was 2.2 %, which can be compared to the 5.3 % contribution from local road traffic. The relative contribution of
the regional shipping was 10.3 %. The mortalities due to the exposure to
NO2 associated with shipping were calculated to be 2.6 premature deaths yr-1. The relative contribution of local and 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 a negative number of premature
deaths. Our study shows that overall health impacts of regional shipping can
be more significant than those of local shipping, emphasizing 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 (SECAs) fuel sulphur limit from 1 % to 0.1 % in
2015, leading to a strong decrease in the formation of secondary particulate
matter on a regional scale was an important step in improving the air
quality in the city.
Shipping is a significant 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 significant species emitted are sulphur oxides (SOx),
nitrogen oxides (NOx) and particulate matter (PM) as well as to some extent
carbon monoxide (CO) and volatile organic compounds (VOCs). Since nearly 70 % of ship emissions occur within 400 km of coastlines (Corbett et al.,
1999), the largest contributions of shipping to air pollution are
concentrated in coastal regions with intensive ship traffic and in 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 9.4 %
of concentrations of primary PM2.5 (particulate matter with a median
aerodynamic diameter less than or equal to 2.5 µm) and 12.3 %
of the concentrations of secondary inorganic particulate matter in Europe
between 1997 and 2003 (Andersson et al., 2009).
Emissions from international shipping are controlled by the
International Maritime Organization (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. Sulphur is regulated through the 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 % has applied since 1 January 2020; before that it was
3.5 %. However, the Baltic Sea, the North Sea and the English Channel are
so-called Sulphur Emission Control Areas (SECAs), where more stringent 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 h has applied for all EU ports. Sweden has also introduced economic
incentives for the reduction of shipping emissions in the form of differentiated
fairway and port fees with a discount for ships using emission control
technologies, which has redounded to a relatively large share of ships with
NOx abatement technology in the region. In 2020 the global cap for
FSC has been decreased to 0.50 %. In 2021 a Nitrogen Emission Control Area
(NECA) will enter into force in this area, with a mandatory Tier 3 standard (80 % reduction compared to Tier 1) for ships built in 2021 and later
that operate the region.
In the Baltic Sea and the North Sea intensive ship traffic results in
high emissions of air pollutants and contributes to high atmospheric
concentrations 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 is highest along the
coasts of southern Sweden, the south-western coast of Finland and the coast
of Estonia, accounting for 25 %–40 % of annual mean concentrations (Jonson et al., 2019). Jonson et
al. (2019) found that Baltic Sea and North Sea shipping also contributed
significantly to concentrations of particulate matter (highest
contributions of 6 %–12 %, allocated to similar areas as NOx) and to the
deposition of sulphur (highest contributions of 10 %–20 %) before the
strengthening of the SECA fuel sulphur limit. They have also shown that the
strengthened limit on the fuel sulphur content in 2015 from 1.0 % to 0.10 %
brought a significant decrease in emissions as well as contributions of
shipping to air pollution through SO2 and to S deposition (maximum
contribution of about 2 %) and to a reduction of the contribution of shipping to
the concentrations of PM. Aulinger et al. (2016) and Matthias et al. (2016)
studied the impacts of current and future (2030) North Sea shipping on air
pollution and found the contributions to be consistent with Jonson et al. (2019; highest NO2 contributions of 25 % and 15 % in summer and winter,
respectively, ozone increased by 10 % along the Scandinavian coast). By 2030,
the contribution of shipping to 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 (Matthias et al., 2016).
Several studies have assessed the impacts of shipping on human exposure to air
pollutants as well as the associated health impacts. Andersson et al. (2009) evaluated impacts of different source regions and of emissions from international shipping on personal exposure to particulate matter across Europe with the help of the atmospheric
chemistry transport model MATCH and assessed relative increase in death rates from this exposure. They found that before the introduction of a SECA in the region, shipping contributed 5 % of the
population-weighted average concentration (PWC) of primary PM2.5 and 9 % of the PWC of secondary inorganic particles. For individual
countries in Northern Europe the contribution to PM exposure varied between
3 % and 19 %. Jonson et al. (2015) assessed the health impacts of
PM2.5 associated with emissions from ships in the Baltic Sea and the
North Sea in the years 2009 and 2011, i.e. before and after the SECA FSC limit
was strengthened from 1.5 % to 1.0 %, with the help of the European Monitoring and Evaluation Programme (EMEP)
chemistry transport model. The relative contributions of shipping to
population exposure to PM2.5 were found to be between 1.6 % and 12 %
for 2009 and between 1.4 % and 10 % for 2011 for the riparian countries,
decreasing by 0 %–40 % between 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 the year 2009 were 14 %–64 %
lower than those found in Andersson et al. (2015), accounting for the fact that, apart
from differences in the models and meteorological years used in the two studies,
Andersson et al. assessed the impact of all European shipping prior to SECA
regulation entering into force, while Jonson et al. assessed the impact of Baltic Sea and North Sea shipping after the introduction of the 1.5 %
SECA fuel sulphur content limit. Barregård et al. (2019) assessed the impact
of shipping in the Baltic Sea for emission years 2014 and 2016, i.e. before
and after the strengthening of the SECA FSC limit from 1.0 % to 0.1 %, using
the EMEP model and showed that exposure to PM2.5 associated with
Baltic Sea shipping decreased by 34 % in the region due to this abatement
measure. Using emissions representative for the year 2016 Barregård et al. (2019) found that shipping contributed 10 % of the population exposure of PM2.5 in the coastal regions
but only less than 1 % in more remote inland areas.
The methodologies for the calculation of the health impacts of PM2.5 in the studies discussed above vary both in the exposure–response functions (ERFs)
used and how the years of life lost are calculated from statistics of
mortalities and life tables. The most common ERF used is the one recommended
by the Health Risks of Air Pollution in Europe (HRAPIE) study (WHO, 2013a), increased risk of all-cause mortality per µg m-3 increase in PM2.5 concentration of 1.0062 (95 % confidence interval is 1.004–1.008), which is almost the
same as the ERF from Pope et al. (2002). Several studies use a higher ERF
presented in Jerrett et al. (2005) and in the European Study of Cohorts for Air Pollution Effects (ESCAPE; Beelen et al.,
2014), both of very similar value, the latter being 1.014 (95 % confidence interval is
1.004–1.026) per µg m-3. Andersson et al. (2009) calculated
the increase in death rates from exposure to particulate matter in Europe using the
ERF from Pope et al. (2002) for the secondary inorganic aerosol and the ERF
from Jerrett et al. (2005) for the primary PM2.5, reasoning that the ERF
of Jerrett et al. based on intra-city gradients better represents the
impact of primary PM2.5, while Pope et al. (2002) uses the inter-city
differences, reflecting more the impact of secondary PM. Combining the increase
in mortality from particulate matter in the EU27 and the relative contribution
of shipping to the exposure to primary and secondary inorganic PM, Andersson
et al. (2015) found the resulting impact of shipping on mortality to be 22 000
premature deaths per year. Jonson et al. (2015) used the Regional Air Pollution Information and Simulation (RAINS) methodology,
which calculates years of life lost (YOLLs) over the expected lifetime of
the population at risk, in this case the population above 30 years, accumulating
YOLLs between the ages of 30 and ca. 80 years (Amann et al., 2004). The
RAINS methodology uses the ERF recommended by the HRAPIE project (WHO, 2013a). As a result,
Jonson et al. (2015) estimated 0.1–0.2 YOLLs per person in areas close to
the major ship tracks resulting from ship emissions in the Baltic Sea
and the North Sea for the year 2010. Barregård et al. (2019) estimated that
187–421 premature deaths per year, corresponding to 0.01–0.02 YOLLs per
person, could be associated with contributions of Baltic Sea shipping
emissions to concentrations of PM2.5 in the year 2014. The lower and higher
estimates used the ERF from the HRAPIE project (WHO, 2013a) and Beelen et al. (2014), respectively.
In our study the impacts of exposure to shipping-related air pollutants on
the health of people living in the Gothenburg region have been assessed using
the ALPHA-RiskPoll (ARP) methodology (Holland et al., 2013; Åström et
al., 2018), which uses the ERFs from the HRAPIE project (WHO, 2013a).
The city of Gothenburg is located on the western coast of Sweden and has about
0.57 million inhabitants and an area of 450 km2. The dominant wind
direction in Gothenburg is south-west with an 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 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 influence 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 6000 and
6500 calls per year, and an additional 600–700 ships pass to 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 continuously decreased from 1990 to 2015 except for in 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 an aerodynamic diameter of less
than or equal to 10 and 2.5 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 months and -1 month, respectively; 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 long-range transport,
and the dominating local sources were road traffic and residential wood
combustion, while the contribution from local shipping was small, with a contribution to the population-weighted annual mean PM2.5 of 0.04 µg m-3. The exposure
of PM2.5 from shipping in other harbour cities in Sweden is lower than
in Gothenburg, with 0.02 µg m-3 in Stockholm and 0.01 µg m-3 in Umeå (Segersson et al., 2017).
This study has been conducted within the BONUS SHEBA (Sustainable Shipping and
Environment of the Baltic Sea Region) project, in which the impact of current and
scenario emissions from ships on air quality has been investigated as
part of a holistic assessment framework for impacts of shipping on marine
and coastal environments. Shipping-related air pollution has been
investigated on a range of spatial scales and with several chemistry transport
models: coarse spatial 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 simulations using high spatial resolution were
used for several harbour cities (Ramacher et al., 2019). The present study
(Part 1) evaluates the contributions of regional and local shipping to the
concentrations of SO2, NO2, PM2.5, O3 and secondary PM
as well as human exposure and the associated health impacts in
Gothenburg for the year 2012. Health impact studies for shipping emissions
in cities are rare mainly because the spatial resolution of the regional
CTMs (chemical transport models) does not allow for city-scale resolution.
This study provides the city-scale health impact assessment (HIA) and
identifies and addresses potential health impacts associated with local and
regional shipping. The studied year (2012) has been considered as a
present-day “normal year” for the Baltic Sea region in terms of meteorological
conditions in BONUS SHEBA. In terms of ship emission regulations, the study
presents a situation with a 3.5 % FSC global limit and a 1.0 % FSC limit in
the SECA area, whereas a 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 the year 2040 are discussed further in
Ramacher et al. (2020; Part 2).
MethodologyModel 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, 2008a) was used. TAPM consists of a meteorological and an air
pollution component. The meteorological component of TAPM is an
incompressible, non-hydrostatic, primitive equation model with a
terrain-following vertical sigma coordinate for 3-D simulations. The model
solves the momentum equations for horizontal wind components, the
incompressible continuity equation for vertical velocity and scalar
equations for potential virtual temperature and specific humidity of water
vapour, cloud water/ice, rain water and snow. The turbulence terms in these
equations have been determined by solving equations for turbulence kinetic
energy and eddy dissipation rate and then using these values to represent
vertical fluxes by a gradient diffusion approach (Hurley, 2008b). Using
predicted meteorology and turbulence from the meteorological component, TAPM
applies a Eulerian grid module in its air pollution component, which consists
of nested grid-based solutions of the Eulerian concentration mean equations
representing advection, diffusion, chemical reactions and emissions. Dry and
wet deposition processes for gases and PM are also included. It includes
gas-phase photochemistry based on the generic reaction set (Azzi et al.,
1992), gas- and aqueous-phase chemical reactions for SO2, formation of
ozone from NOx and non-methane volatile organic compounds (NMVOCs; treated as VOC reactivity). The
photochemistry mechanism also captures the important features of the
secondary particle formation, i.e. formation of sulphate and nitrate
following SO2 and NO2 oxidation as well as formation of
secondary organic aerosol as a fixed part of the degraded smog reactivity
representing VOC species in the reaction scheme of TAPM (Hurley, 2008b).
In this study, the meteorological component of TAPM was driven by the
recently published European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis ERA5 synoptic meteorological reanalysis ensemble
means with 30 vertical layers, 0.3∘×0.3∘
horizontal resolution and 3-hour temporal resolution. For the year 2012,
five nested domains have been simulated with the synoptic meteorological
component with the innermost meteorological fields of a 30 km ×30 km domain with 500 m resolution (Fig. 3a). In addition, the observed wind
fields at four meteorological sites were assimilated to nudge wind speed and
wind direction calculations in the innermost domain. In TAPM, an Exner
pressure function is integrated from mean sea level to the model top (10 Pa
in this study) to determine the top boundary condition. The Exner pressure
function is determined from the sum of the hydrostatic component and
non-hydrostatic component (Hurley, 2008b). The number of vertical grid levels
was 30 in this study. Twenty of these layers are below approximately 2 km;
the lowest layer extends to ca. 10 m above ground.
The spatial resolution of the city-scale CTM was 250 m ×250 m with
the local coordinate system SWEREF 99 12 00 (Landmäteriet, 2020), and the size of the CTM domain was
about 25 km ×25 km, covering the city of Gothenburg and the
harbour area along the shores of the Göta älv running through the city
(Fig. 1a).
The chemical boundary conditions were taken from the Community Multiscale
Air Quality Modeling 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 (Fig. 1b), were
driven by the high-resolution meteorological fields of the
Consortium for Small-scale Modelling (COSMO) Climate Limited-area Modelling Community (CLM; Rockel et al., 2008) version 5.0 using the ERA-Interim
reanalysis as forcing data. Chemical boundary conditions for the CMAQ model
simulations were provided through hemispheric CTM simulations from a System for Integrated Modelling of Atmospheric Composition (SILAM)
model (Sofiev et al., 2006) run on a 0.5∘×0.5∘ grid resolution, which was provided by the Finnish Meteorological
Institute (FMI). Land-based emissions for the regional-scale simulations
were represented by hourly gridded emissions calculated with the SMOKE-EU (Sparse Matrix Operator Kerner Emissions, EU version)
emission model (Bieser et al., 2011). The SMOKE-EU emission data are based on
reported annual total emissions from the European Pollutant Emission
Register (EPER), the official EMEP emission
inventory (https://www.ceip.at/, last access: 11 June 2020) and the EDGAR HTAP (Emission Database for Global Atmospheric Research, project of the Hemispheric Transport of Air Pollution task force) v2 database (EPER, 2018; CEIP, 2018; Olivier et
al., 1999). For shipping emissions, the model used an inventory calculated
with the Ship Traffic
Emission Assessment Model (STEAM) consistent with the inventory used by TAPM for the
city-scale, calculated with 2 km ×2 km grid resolution (STEAM3,
Johansson et al., 2017); more details are given in the next section. The
STEAM version used for the CMAQ simulations did not, however, include VOC emissions. As chemical boundary conditions, vertical model
layer 7 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 TAPM
allows just 1-D boundary concentration fields with hourly time resolution,
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 domain of TAPM (Fridell et al., 2014).
The city-scale model set-up is summarized in Table 1.
City-scale model set-up.
DomainSpatial resolutionsModel/databaseMeteorology30 km ×30 km500 mECMWF ERA5 0.3∘×0.3∘, 21 layersBackground concentrations160 km ×96 km4 km ×4 kmCMAQLocal shipping emissions30 km ×30 km250 m ×250 mSTEAM2Local traffic emissions30 km ×30 kmMetres (line sources)Miljöförvaltningen and HBEFA v. 3.2Local industrial, machines,30 km ×30 km1 km ×1 kmSMEDwood burning and aviation etc.Emission inventoryRegional and local shipping emissions
Shipping emissions were calculated with STEAM, taking into account individual vessel characteristics and vessel
activity data (Jalkanen et al., 2009, 2012 Johansson et al., 2017) based
on detailed information of technical parameters of individual vessels and
position data of individual ships taken from reports from the Automatic
Identification System (AIS) of the Helsinki Convention (HELCOM) member
states. STEAM calculated fuel consumption and emissions as functions of vessel activity; the STEAM3 version of the model has been used
(Johansson et al., 2017) with an additional module for the calculation of VOC
emissions. The emission inventory includes combustion emissions from all
engines and appliances on ships (boilers, auxiliary and main engines). The
emission inventory for local shipping around Gothenburg consists of
hourly emissions from ships on 250 m ×250 m grid resolution. STEAM provided shipping emissions for the year 2012 for the compounds
NOx, SOx, CO, CO2, VOC and PM2.5. PM2.5 is further
divided into elemental carbon (EC), organic carbon (OC), SO42- and
mineral ash. Ship emissions were provided in two vertical layers with
emissions below and above 36 m height in order to differentiate between
emissions from large ships with high stacks and the smaller vessels with
lower stack heights (Fig. 2). The stack release heights were attributed to
the corresponding midpoints of model layers in TAPM: 15 m for the emissions
below 36 m height and 36 m for emissions above 36 m.
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 the HBEFA v. 3.2 (Handbook Emission Factors for Road Transport;
Rexeis et al., 2013). The HBEFA comprehends emission factors for different
classes of road vehicles based on type of vehicle (e.g. motorcycles,
light-duty vehicles, heavy-duty vehicles), technology or fuel (e.g. petrol, diesel,
hybrid) and emission standard (pre-Euro, Euro 6). For each of those, a
number of road categories and driving patterns that affect emissions are
also specified within the vehicle sub-segments. These emission factors also include emissions of wear particles as well as evaporative VOC emissions. 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 motorcycles,
heavy-duty vehicles and busses on road links with specified type, speed and
congestion hours) from the database of the Environmental Administration,
City of Gothenburg (Miljöförvaltningen), and corresponding emission
factors calculated in the HBEFA database. These data were applied as line
emission sources in the model. Resuspension of the road dust is not covered
in the model.
Other emissions
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 Emissions Data (SMED, 2015)
for 2012.
Emissions from the following sectors were geographically distributed on a 1 km ×1 km grid and assigned an 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” or “other transports” (the landing and take-off
emissions from aviation, trains and military). They also stem from the SMED
database and were obtained gridded on the 1 km ×1 km grid from
the SMHI (Swedish Meteorological and Hydrological Institute). These emissions
were applied as gridded sources in the model.
The local emissions of NOx, SO2, PM10 and VOCs from the
above-mentioned sectors in the model domain are shown in Fig. 3. Local
shipping was the dominant emission source of SO2, contributing 61 % of the total SO2 emissions (502 t yr-1) in the model domain.
Further, local shipping contributed 41 % of NOx emissions
in the model domain, which was comparable to the contribution from road
traffic (47 %) to total NOx emissions of 5072 t yr-1. However,
road traffic was the most major contributor of PM10 in the model domain
(45 % of 357 t yr-1), while local shipping and industry contributed
approximately 25% each. For VOCs (7457 t yr-1), about 92 % of
emissions were released from the industrial sector and only 0.3 % from
local shipping.
Proportions of different source categories in the local emission
inventory for the city-scale model domain in the year 2012. The total emissions
are 502 t yr-1 for SO2, 5072 t yr-1 for NOx,
357 t yr-1 for PM10 and 7457 t yr-1 for VOCs.
Design of model simulations
Several model simulations were performed to investigate the influence of
shipping within the city domain and influence of regional shipping
outside the city on air pollution in 2012:
a simulation including complete emission inventory both in the city-scale
simulation and in the CMAQ simulation supplying the chemical boundary
conditions (“base scenario”)
a simulation excluding local shipping in the city domain of TAPM but
including regional shipping chemical boundary conditions in the CMAQ
simulation (“no local shipping scenario”)
a simulation excluding both local shipping in the city domain of TAPM and
shipping in the chemical boundary conditions in the CMAQ simulation (“no
local and regional shipping scenario”).
In addition, three sensitivity studies were performed within this study:
“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 have only recently been included in STEAM.
“Primary PM from local shipping” had the same emission input as the
“base scenario”, but for local shipping only the primary PM emissions
as calculated by STEAM were included. All emissions of the gaseous
species were excluded, preventing the formation of the secondary PM from local
shipping. The difference between “base scenario” and “primary PM from
local shipping” reflects the formation of the secondary PM from SO2,
NOx and VOC emitted by local shipping.
“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 city traffic.
Model evaluation
Model evaluations were carried out for both meteorological and air pollution
parameters. The simulated meteorological parameters (temperature, relative
humidity, wind fields and precipitation) were evaluated with measurements at
four stations: Femman (57.70∘ N, 11.97∘ E; 30 m a.s.l.),
Göteborg A (57.72∘ N, 11.99∘ E; 3 m a.s.l.), Vinga A
(57.63∘ N, 11.60∘ E; 18 m a.s.l.) and Landvetter
(57.68∘ N, 12.29∘ E; 154 m a.s.l.). The urban background
site Femman is located on a rooftop in the city centre, and the local
meteorological variables as well as the air quality data are continuously
measured by the Environmental Administration in Gothenburg
(Miljöförvaltningen), while the other three meteorological stations
are driven by the SMHI. For the air pollution evaluation, Femman, Haga and
Mölndal were included. Haga is located in a one-sided street canyon in
central Gothenburg with a park to the east of the station. Mölndal is
located in southern part of Gothenburg on a rooftop about 30 m above ground
and corresponds to a traffic station. NO2 is measured via a reference
chemiluminescent method at Femman and Haga and the differential optical
absorption spectrometry (DOAS) method at Mölndal. PM10 mass
concentrations are measured by a TEOM (tapered element oscillating
microbalance; Thermo Fisher Scientific, model 1400ab) instrument at all three stations. The ozone
instrument at Femman was Teledyne (model T400) and at Mölndal a DOAS
from OPSIS. Hourly averaged air quality data for NO2, PM10,
PM2.5 and O3 at the three air quality stations were used to
evaluate the model performance.
The FAIRMODE DELTA tool version 5.4 was used for the evaluation of the model
results for the city of Gothenburg. The DELTA tool is an IDL (interface
definition language)-based statistical evaluation software which allows for the performance diagnostics of the air quality and meteorological model
(Thunis et al., 2012; Pernigotti et al., 2020). The tool focuses on the air
pollutants regulated in the Air Quality Directive 2008 (AQD 2008) and
calculates statistical performance indicators such as mean, exceedance,
normalized mean bias (NMB), normalized mean standard deviation (NMSD) and
high percentile (Hperc; see Sect. S1 in the Supplement). Moreover, a
performance criterion can be calculated that combines 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 (Pernigotti et al., 2020). 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 criterion. We
applied the DELTA tool to concentrations of NO2, PM2.5, 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 criterion.
Health impact assessment
The health impacts of exposure of the population in Gothenburg to
shipping-related air pollutants were assessed with the ALPHA-RiskPoll (ARP)
methodology (Holland et al., 2013), which provides for the calculation of a wide range
of air-pollutant-specific health effects based on the population-weighted
concentrations, national population statistics on age distribution of
the population, mortality and morbidity data and effect-specific
exposure–response relationships. The methodology has been developed and used
for the quantification and assessment of the benefits of air pollution controls
in Europe for the United Nations Economic Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution
and is based on work for the Clean Air For Europe (CAFE) programme and on the
EU project Modelling of Air Pollution and Climate Strategies (EC4MACS).
Following WHO recommendations (WHO, 2013a) and the CAFE cost–benefit analysis methodology for the assessment of health
impacts of air pollutants; the impacts have been considered following WHO recommendations and CAFE. Exposure to these three
pollutants is considered most harmful by the World Health Organization (WHO,
2013b). In this study only the most serious impacts (i.e. loss of life)
are presented, taking into account the impacts of long-term exposure to
PM2.5 and short-term exposure to ozone and NO2, i.e. the impacts
marked A* in the HRAPIE study (WHO, 2013a). The indicator SOMO35 is
used for ozone, representing the yearly sum of the daily maximum of the 8-hour running average above a threshold of 35 ppb. The health impacts of some
pollutants are correlated, and that is why the premature deaths attributed to
each pollutant cannot simply be added up. In particular, it has been
estimated that adding premature deaths attributed to PM2.5 to those
attributed to NO2 could result in double-counting of around 30 %
(WHO, 2013a). The health impacts calculated with the ARP model are presented
as premature deaths and YOLLs per year using the ER function of the model,
i.e. 1.0062 (95 % confidence interval is 1.004–1.008) per µg m-3 (WHO, 2013a).
The concentration fields of PM2.5, O3 and NO2 were
calculated by the coupled high-resolution (250 m ×250 m) modelling
system as described above. Annual means and SOMO35 were calculated from
hourly concentrations for each grid. Population data at 1 km ×1 km
resolution were obtained from Statistics Sweden (SCB) for 2015, with a
population of 572 779 in the city of Gothenburg. As there were no significant
changes in population density between 2012 and 2015, the population data for
2015 were used. Population-weighted average concentrations (PWCs) for the
model domain were calculated by multiplying the modelled annual mean
concentration of the pollutant on each grid cell by the population in the
same grid cell as weight for the modelled concentration.
To calculate the health risks, 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 are also needed. 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 the WHO (2013a):
6.2 % (95 % confidence interval is 4.0 %–8.3 %) relative risk increase
per 10 µg m-3 of increased exposure for the PM2.5 exposure,
0.29 % (95 % confidence interval is 0.14 %–0.43 %) relative risk
increase per 10 µg m-3 of increased exposure for the ozone exposure
and 0.27 % (95 % confidence interval 0.16 %–0.38 %) relative risk
increase per 10 µg m-3 of increased exposure for the NO2
exposure. The ARP uses linear ERFs, recognizing the limited range of pollutant
exposures in Europe. The YOLLs are calculated per year, applying the
relative risk within national life tables. This is done through relation
between the years of life lost per 100 000 people per unit PM2.5
concentration and the life expectancy of the population developed by Miller
et al. (2003) based on analysis of the life tables. The premature deaths are
calculated using the ERF for all-cause mortality and the total national
mortality rates. This methodology is justified for European countries with
a health status and proportion of natural mortality of the population
corresponding to the population studied in the epidemiological studies which
brought forward these ERFs. For regions with high concentration levels of
PM2.5 the HIA studies need to use different forms of ERFs, and for
populations with a different health status and proportion of natural mortality
compared to the US and Western Europe, cause-specific rather than all-cause
mortalities need to be used. In this study the analysis was made separately
for the population exposure related to the different pollutants from local
and regional shipping.
Results and discussionModel evaluation
The model evaluation was conducted for both meteorology and air pollution in
the innermost model domain. The comparison between hourly measured and
modelled local meteorological parameters (temperature, relative humidity,
total solar radiation, wind speed, wind direction and precipitation) shows
a high correlation and low bias: averaged over all available stations,
temperature and wind speed are slightly underestimated at -0.46∘C and -0.18 m s-1, respectively. A detailed analysis can be found in Table S1
in the Supplement. The application of ERA5 datasets in the model shows
significant improvements from the default reanalysis datasets. Nevertheless,
the predictions of the meteorological parameters such as wind field 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, indicating the model's good capability of reproducing local wind field except for the missing ∼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 the 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 %–8 %).
Comparison between measured and modelled winds at Femman station:
(a) the observed wind rose in January and July and (b) bias of the wind speed
based on the difference between simulated wind speed and measured wind speed. For example, a
positive bias from 0 to 2.5 m s-1 in wind direction N has a frequency
of almost 30 %.
The evaluation of ambient pollutants was conducted through the major
statistical parameters (Table S2). At the urban background
site Femman, the estimation of NO2, PM10 and PM2.5
concentrations was satisfactory in summer, with a lower bias (-0.16µg m-3); however the model tended to underestimate NO2,
PM10 and PM2.5 concentrations in winter (-15.35µg m-3). O3 evaluation was carried out at Femman and Möldal stations, and underestimation of the daily maximum of the 8 h
means was also detected, which could be caused by the 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 the road site Haga fulfil the performance
criteria for the statistic indicator Hperc (Fig. 5). The indicator
Hperc indicates the model's capability of reproducing extreme events,
represented by a selected high percentile for modelled and observed values. A
detailed evaluation of simulated concentrations in the form of scatter plots of
modelled versus measured daily concentrations can be found in Fig. S1 in the
Supplement.
Summary statistics of model performance for the annual mean values
of (a)NO2 (hourly values), (b) PM10 (daily values), (c) PM2.5 (daily values) and (d)O3 (daily maximum of the 8 h
means), including days of exceedances. Stations (blue dots) within the green
bars: performance criteria satisfied; stations within the orange bars: performance
criteria satisfied, error dominated by the corresponding indicator. Green
light: > 90 % of the stations fulfil the performance criteria.
Red light: < 90 % of the stations fulfil the performance
criteria. The indicator Hperc indicates the model's capability of reproducing
extreme events, represented by a selected high percentile for modelled and
observed values.
The underestimation of NO2 and PM10 especially at road sites
demonstrates the impact of too coarse spatial resolution (250 m ×250 m)
not capturing high concentrations at the street level, possible missing or
insufficient cover of local emissions like resuspension particulate matters
from traffic sources, and incomplete chemical reactions in the model etc. As
pointed out by Karl (2018), recent nested model approaches have not
resolved the details in emission processing and near-field dispersion at
the street and neighborhood level. However, shipping emissions are, when
reaching the exposed population, more dispersed, and the 250m×250m 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.
Impact of ship emissions on local air qualitySO2
The study was performed for 2012 conditions, when the sulfur content in
marine fuels was limited to 1 % in the region and 0.1 % for ships at
berth. With these fuel sulphur limits, local shipping is still the
dominant local emission source of SO2 (60 %) and influences the area
around main shipping routes and city ports (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 (13 %) to the
model domain average and between 0.3 ppb and up to 0.6 ppb in a wide area
around the main shipping routes and ports. The impacts were higher in summer
than in winter (Fig. S2) as a result of higher shipping
emission in summer as well as of differences in the meteorological situation.
The highest SO2 contributions (maximum of 0.7 ppb to the annual mean and 0.8 ppb in summer) were found around the major ports (Älvsborgshamnen,
Skandiahamnen, Skarvikshamnen, Ryahamnen, Lindholmshamnen and Frihamnen) along
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 side of the river
due to the dominant south-westerly winds. The regional ship emissions
outside the model domain contribute an additional 0.06 ppb (15 %) to
the model domain average. In contrast to local shipping, this
contribution is distributed rather evenly over the model domain.
The modelled SO2 concentrations in Gothenburg are relatively low, and
Fig. 6 shows the highest concentrations around the city ports as well as around
industrial areas north of the Göta älv. The dominated south-westerly
winds transport emissions from the shipping routes and port areas further
inland to the north. Today, Eriksberg, located on the north waterfront of Göta
älv, is a modern residential and commercial centre built at the site of a former dockyard area. We have selected this place to study the relative
impact of shipping in more detail. The shipping-related monthly
contributions to SO2 concentrations at Eriksberg were 47 % on average
and over 60 % between June and August. Figure S3 shows the
modelled monthly mean relative contributions at Eriksberg.
NO2
NOx is mainly emitted as nitrogen oxide (NO); in STEAM the
NO2/NOx ratio is 5 %. In the 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 photostationary 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 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 (14%) and up to 3.3 ppb in areas
with a high contribution (Fig. 7b). The relative contribution of local
shipping to the NO2 concentrations in the model domain in Gothenburg is
comparable with 11 % in Riga (Latvia), 16 % in Gdańsk–Gdynia
(Poland) and 22 % in Rostock (Germany) in 2012 (Ramacher et al., 2019). The
calculated model domain mean contribution to NO2 concentrations from
regional shipping is 1.0 ppb (26 %) and up to 1.2 ppb in the most heavily
impacted areas (Fig. 7c), which is larger than local shipping contributions.
The total shipping-related relative contribution in Gothenburg to the
NO2 annual mean in the model domain is 40 %. In
summer the contribution reaches 49 % (17 % from local shipping +
32 % from regional shipping) to the average summer NO2 concentration
in the model domain, and influenced areas expand further inland. This is
the result of 20 % higher summer emissions compared to winter, a different
photochemical state and different local meteorological conditions
(Fig. S4).
Nearly 90 % of NOx emissions in Gothenburg are from road traffic
(47 %) and local shipping (41 %). The impact of local shipping is
concentrated in areas inside the harbour along the Göta älv and
decreases with growing distance to the port areas. Figure 8 presents the impacts
of local and regional shipping as well as of road traffic and all
other anthropogenic sources (including NO2 coming from the model domain
boundary) on monthly levels at Eriksberg. The modelled annual mean NO2
concentration from all sources is 7.5 ppb at Eriksberg, of which 2.5 ppb
(33 %) originates from local shipping, 1.0 ppb (13 %) from regional
shipping and 2.1 ppb (28 %) from road traffic. The maximum relative
contributions from local shipping and regional shipping to the monthly mean
concentrations reach 43 % in July and 16 % in June. Together, the monthly average
contributions from local and regional shipping are larger than
or comparable to the contributions from road traffic in all months. Even
though road traffic is a major contributor to the NO2
concentrations in an urban environment, local ship emissions are of major
concern, especially in areas close to the city ports.
Modelled monthly mean contribution of the local shipping,
regional shipping, local road traffic and other anthropogenic emissions
(including the contribution from the boundary conditions) to the NO2 concentrations at Eriksberg in the year 2012.
O3
O3 is formed in photocatalytic cycles involving NOx, ozone and
hydrocarbons through the photolysis of NO2 in sunlight. The same cycle
also involves the 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 a focus on summer months
(JJA). The regional background concentration of ozone at the regional background
station Råö, close to the Gothenburg area, was 37 ppb in the summer of
2012. Modelled summer ozone levels in the model domain are in the 15–30 ppb
range (domain average is 28.6 ppb; Fig. 9a). Since NOx is mainly
emitted as NO, the emissions from local shipping cause local reduction of
ozone concentrations by 0.5 ppb (∼2 %) in the main
shipping routes and port areas due to the titration of O3 by NO (Fig. 9b). The maximum O3 depletion along the north riverbank of the Göta älv
is 4 ppb (∼14 %) with
both NOx and non-methane volatile organic compound (NMVOC) emissions from local shipping (Fig. 9b), while regional shipping
emissions increase the ozone concentrations by 1 ppb over the land. This
ozone increase can be compared to the 4–6 ppb increase caused by shipping
emissions over the remote ocean as a result of large-scale summer ozone
production found by Huszar et al. (2010, Fig. 9c).
In the local STEAM inventory, the NMVOCs from shipping were introduced. These NMVOCs serve as precursors of
O3 and enhance photochemical ozone production. TAPM uses the concept
of VOC reactivity instead of individual NMVOCs, producing a pool of peroxy
radicals which take part in the ozone production photocatalytic cycle. A
sensitivity run was performed to study the impact of VOC emissions from
local shipping on ozone concentrations in the city by excluding the local
shipping VOC emissions from the simulation. Figure 9d shows the impact of the
VOC emissions: the O3 concentrations increase by up to 2 ppb
(∼7 %) along the main shipping routes and the port areas,
which means that the titration effects of NOx emissions from local
shipping on the ozone concentrations were maximally 6 ppb when VOC emissions
were excluded, compared to 4 ppb in the Base simulation. Sensitivity of
ozone formation to VOC emissions also clearly indicates that the city centre
is most often in a VOC-limited regime. Further details of the impact of
shipping emissions on ozone formation are illustrated in Fig. S5, showing summer ozone formation from regional and local shipping
as well as from local shipping VOC emissions at Eriksberg. At this
location local shipping emissions almost always lead to ozone depletion.
In contrast, VOC emissions from local shipping cause the increase in ozone
concentrations, confirming that the location is in a VOC-limited
photochemical regime. Regional shipping tends to increase local
ozone concentrations on most days (78 d between June and August).
Inspecting the details of the diurnal variation of ozone contributions (Fig. S5b–d), one can see that during the rare occasions without ozone depletion
by local shipping, there is a small amount of ozone formation from local
shipping emissions and no ozone formation from local shipping VOC
emissions, indicating the presence of a NOx-limited regime (Fig. S5b), whereas during
most of the studied days local shipping emissions have an ozone
depletion effect during daytime, while the ozone formation effect of local shipping VOC emissions peaks in the morning and sometimes also in the afternoon
(Fig. S5c). Regional shipping increases the ozone concentrations in all
three depicted cases, showing maxima in the afternoon.
Particulate matter
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 is 4 µg m-3 as the model domain average (Fig. 10a).
Local shipping emissions contributed 0.1 µg m-3 (3 %) of
the annual mean as the model domain average (Fig. 10b), which had the same
relative contribution as 3 % in Gdańsk–Gdynia and higher than 1 % in
Rostock and Riga in 2012 (Ramacher et al., 2019). Under 2012 conditions, regional shipping was a larger contributor to the local PM2.5 than local shipping, with an annual mean average contribution of 0.4 µg m-3 (11 %; Fig. 10c). The total shipping-related relative contribution
to the annual averaged PM2.5 concentrations in the model domain was 14 % and in the summer reached 27 % (4 % from local shipping +23 % from regional shipping) of the summer averaged PM2.5
concentrations in the model domain (Fig. S6). At the near-harbour
residential area Eriksberg the modelled annual mean PM2.5 concentration
from all sources is 4.5 µg m-3. The calculated annual mean
contributions from local shipping and regional shipping are 0.2 µg m-3 (∼4 %) and 0.4 µg m-3
(∼9 %), respectively. The maximum monthly relative
contribution from local and regional shipping was about 29 % in July,
of which 21% was from regional shipping (Fig. 11). Road traffic, the largest
local source of PM10, contributed up to 5% of monthly PM2.5
mean concentrations. The large contribution of PM2.5 from regional
shipping is in agreement with the character of source apportionment in Gothenburg.
An early study shows that the main source types of PM2.5 in Gothenburg
were long-range transport (LRT; about 50 %) followed by ship emissions
(20 %) and local combustion (19 %) between 2008 and 2009 (Molnár et
al., 2017).
The secondary aerosol formation in TAPM is heavily parameterized; however,
it captures the important features of the secondary particle formation, i.e.
formation of sulphate and nitrate following SO2 and NO2
oxidation as well as formation of SOA as a fixed part of the degraded smog
reactivity representing VOC species in the reaction scheme of TAPM (Hurley, 2008b). On an urban scale, formation of secondary PM is usually
suppressed as the radical pool is depleted by the primary emissions, and many
urban models do not consider the secondary PM at all. 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 local shipping. Modelled secondary PM concentrations
from shipping were calculated as the difference between the base run and
this sensitivity run. Figure S7 shows contributions of max.
2 % of the PM related to local shipping in Gothenburg in the winter months
and negligible contributions in summer. The secondary PM, mainly formed far
from the sources, tends to disperse and accumulate in the eastern part of
Gothenburg due to the prevailing wind directions.
Modelled monthly mean contributions from local shipping, regional
shipping and other sources (including contribution from the boundary
condition) to PM2.5 concentrations at Eriksberg for the year 2012.
Calculation of exposure and health effects from ship emissions
The contribution of emission sources to population exposure depends on the
relationship between population density and air pollution levels. The areas
with relatively high exposure to PM2.5 due to local and regional
shipping are city ports and areas around and 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 local plus regional shipping
as well as for contributions of 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 road traffic
in a larger city area since regional-shipping-related PM2.5 exposure is
evenly distributed over the city (Fig. S8). The sum of the PWC
of PM2.5 from local plus regional shipping is 0.51 µg m-3 in the model domain, of which regional shipping contributes 82 % compared to 0.22 µg m-3 associated with 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 the PWC of
NO2 from regional and local shipping was 1.65 ppb, which is similar to that from
road traffic (1.75 ppb), with gradients in the concentration fields
north of the Göta älv. Because of the effect of local O3
titration by NO emitted by shipping, the exposure to SOMO35 from
shipping was negative along the Göta älv. However, SOMO35 exposure
due to regional shipping was positive, with a population-weighted SOMO35 of 70.9 ppb × h in the model domain, and showed a relatively high level in areas with high
population density.
Population-weighted annual mean concentrations of NO2,
PM2.5 and SOMO35 associated with all sources, road traffic and local and
regional shipping in the city of Gothenburg for the year 2012.
SourcesNO2PM2.5SOMO35(ppb)(µg m-3)(ppb × h)PWC in base simulation4.704.1219 698Road traffic1.750.2212Local and regional shipping1.650.51-1115Local shipping0.680.09-1186Regional shipping0.970.4271
The PWCs for these pollutants were then used in the health impact
calculations, and the results are presented as years of life lost per year and loss
of life expectancy (years of life lost per person)
for PM2.5 and as premature deaths for ozone and NO2. The estimated
loss of life expectancy (YOLLs per person) due to PM2.5 from local
shipping was 0.003, while from the regional shipping it was 0.014. For
comparison, the impact of exposure to PM2.5 from road traffic was
calculated to be 0.007 YOLLs per person and the impact of exposure to all PM2.5 in the base
simulation to be 0.14 YOLLs per person (Table 3). In all, shipping
contributed 12 % of the calculated health impacts from the total
exposure to PM2.5 in the city and the impact was more than 2 times
larger than that of local road traffic, regional shipping being a
larger risk for human health than local shipping (> 80 %)
in Gothenburg. The exposure to ozone related to shipping emissions reduced
acute mortality by 0.4 premature deaths per year due to the NO titration
effect. This effect included 0.03 additional deaths attributed to ozone
formed from regional shipping emissions (Table 3). Exposure to NO2
related to shipping emissions caused 2.6 additional premature deaths yr-1, and the impact of local shipping was similar to the regional impact.
This impact corresponded to 35 % of the impact of the NO2 exposure
in the base simulation and was similar to the impact of road traffic.
Health impacts calculated for O3, NO2 and PM2.5
contributions of local and regional shipping and 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.
PollutantImpactUnitLocalNSBS regionalAllLocal roadTotalshippingshippingshippingtrafficexposureO3Acute mortality (all ages)Premature deaths-0.50.03-0.40.0057.6NO2Acute mortality (all ages)Premature deaths1.061.522.592.737.35PM2.5Chronic mortality (all ages)Life years lost31143174741393Chronic mortality (all ages, ARP)YOLLs per person0.0030.0130.0150.0070.12Chronic mortality (Age 30+, RAINS)YOLLs per person0.0030.0140.0180.0080.14Chronic mortality relative to that from–2 %10 %12 %5 %the total exposureAssessment 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 the calculated pollutant concentrations,
the grid resolution when assessing the population exposure, the general
shape of the 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 YOLL calculations and from the spatial resolution.
To compare the results of Jonson et al. (2015) with the results of this study, the
YOLLs per person from PM2.5 exposure calculated in the ARP were
multiplied by the life expectancy of the population above the age of 30, i.e. 50 years, and divided by the population in the model domain. The health impacts
of PM2.5 were also calculated using the RAINS methodology directly for
the calculated PM2.5 exposures. The results of both methods are presented and are very similar.
The largest uncertainties are associated with the exposure–response
functions (ERFs) as such. In this study impacts for the mean values of ERFs
are presented; the 95 % confidence interval for these functions is given
in Sect. 2. The ERFs used here are those recommended by the WHO (2013a). For PM2.5, ERFs with higher values for spatial analyses of air
pollution and mortality were found by the ESCAPE project 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 is 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). In the ARP a linear form of
ERF is applied, which is justified by a rather narrow interval of PM
exposure levels in Europe. In terms of the impact of the total exposure to
PM2.5 on natural mortality, the linear and log-linear form of the
functions give similar results within the concentration range of 10–30 µg m-3, the linear model giving slightly lower relative risks in
this range and higher relative risks below and above (Ostro et. al., 2004).
The PM2.5 levels found in our study fall below 10 µg m-3. For
regions with high PM2.5 levels different ERF models need to be applied, and
for global HIA studies or studies in other regions of the world than Western
Europe and North America, ERFs for cause-specific mortalities rather
than natural mortalities are usually used. There are two further important
issues regarding the uncertainties associated with the ERFs. First, 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 added 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).
The most robust relationship between air pollution and effects on human
health is for particulate matter (WHO, 2013b). In Swedish cities, including
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 it 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 and dry and wet deposition processes
that need to be described for the whole range of relevant geographical scales and
timescales. 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). Furthermore, the modelled PM concentrations used as the boundary conditions
in this study showed average underestimates of PM2.5 by 60 % and 17 % in the summer and annually, respectively (Karl et al., 2019a). Two
studies addressing impacts of shipping on air pollution in Gothenburg
(Segersson et al., 2017; Repka et al., 2019) assessed the total
concentration levels and contribution of shipping to them; however, none of them
are calculated for the year 2012 assessed in this study. Segersson et al. (2017) show annual mean background PM2.5 concentrations for the year
2011 of about 5 µg m-3 for Gothenburg, reaching concentrations of
>8 µg m-3 in polluted parts of the city. An annual
mean concentration map presented in Repka et al. (2019) for the year 2016 shows
similar concentration levels with background concentrations of about 6 µg m-3 and maximum concentrations of > 8 µg m-3. Both studies used PM10 monitoring data at the urban
background station Femman to inversely derive the boundary conditions for
PM2.5. Jonson et al. (2015, 2019) studied the impacts of Baltic Sea
and North Sea shipping with the EMEP model for the years 2010 and 2016 and found
in both cases annual mean concentration levels on the western coast of Sweden to be
about 4–5 µg m-3. This concentration should correspond to the
background levels of the city-scale simulations and the year 2016. Jonson et al. (2019) also compared the modelled concentrations with background
measurements from the station Råö, situated 20 km south of the city,
and found a model underestimation of 0.7 ppb for the annual mean. The
concentration levels of PM2.5 found in this study were lower than in
Segersson et al. (2017) and Repka et al. (2019), but they agree reasonably
well with Jonson et al. (2015, 2019). Segersson et al. (2017) addressed
the health effects of PM2.5, PM10 and black carbon in three Swedish
cities, among them Gothenburg, using the Gaussian model SIMAIR. The population-weighted exposure to PM2.5 for Gothenburg was calculated to be 6.5 µg m-3, which was associated with ca. 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) for PM2.5
from the city sources. The values can be compared to the population-weighted
exposure to PM2.5 of 4.1 µg m-3, associated with ca. 140
premature deaths found in this study. Jonson et al. (2015) calculated the impact
of shipping emissions in the Baltic Sea and the North Sea using the EMEP
model, and a map presenting the geographical distribution of life expectancy loss
shows approximately 0.2 YOLLs per person around the west coast of Sweden.
This agrees reasonably well with our estimate of 0.18 YOLLs per person
in Gothenburg.
It is important to bear in mind the uncertainties in total concentrations of PM and
other air pollutants when assessing the relative contribution of
shipping to the overall impact of air pollution. Assessments of impacts of
selected anthropogenic 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 the contribution
of shipping to the population-weighted annual PM2.5 concentration to be
0.04 µg m-3 and the contribution of 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, it is important to bear in mind that the studies
assessed two different years.
Conclusions
The impact of local and regional ship emissions 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.
The city-scale model simulations with and without local and regional
shipping in the emission inventory revealed that the impacts from shipping on
air quality in Gothenburg were substantial. The calculated contribution from local shipping to annual mean NO2 concentration was 0.5 ppb, representing 14 % of the calculated annual mean NO2 concentration. Including
the 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 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 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 mean ozone concentration was calculated to be -4 ppb
(∼14 %). The negative effect of solely NOx emissions
from local shipping on the ozone concentrations was up to -6 ppb
(∼21 %) and was 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 ozone formation
in the city, with the highest contribution being 2 ppb (∼7 %)
as a 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 et al., 2019b).
The simulated emissions from local and regional shipping contributed an average of 0.5 µg m-3 throughout the model domain and a maximum of 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. Furthermore, its contribution to the PWC was higher,
contributing 0.4 µg m-3 (10 % of the total PWC for
PM2.5). Contribution from local shipping was 0.1 µg m-3
(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 % is associated with regional shipping
in the North Sea and the Baltic Sea. The shipping impact is more than twice as
high as the modelled impact of PM2.5 associated with local road
traffic. Impacts from exposure to NO2 and ozone were calculated in
terms of premature deaths per year, and 2.6 additional cases yr-1 were
calculated for exposure to NO2, with regional and local shipping
contributing 59 % and 41 %, respectively. The impacts from exposure to
ozone were of opposite magnitude. The decrease in ozone due to NO
titration reduced the calculated mortalities by 0.4 cases yr-1. The
impact of the exposure to PM2.5 from shipping calculated as premature
deaths was 18 cases yr-1. The implementation of the more stringent
SECA regulations for FSC in the year 2015 is not likely to have changed the impacts
of NO2 and ozone. According to the study of Jonson et al. (2019), a reduction in the impact of the regional shipping
contribution to PM2.5 of approximately 35 % could be expected around Gothenburg, while a much
smaller change can be expected in emission from local shipping since
hotelling and inland shipping already use a fuel with 0.1 % FSC in the
model. This would mean similar reductions in the impacts related to
PM2.5 in the city of Gothenburg. The global cap of 0.5 %
for FSC, which entered into force on 1 January 2020, will not have
any significant impact on further reduction in shipping-related air
pollution in Gothenburg compared to the situation after 2015. The global study of
Sofiev at al. (2018) shows that around the western coast of Sweden a decrease in
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 air pollution
in the city.
The impacts of local shipping emissions on air quality and human health are
further discussed in Part 2 of this paper (Ramacher et al., 2020), which presents a study of several future shipping scenarios for the year 2040 that adopt changes in shipping emissions due to changes in ship traffic
volumes and legislation on emissions of air pollutants at sea and on energy
effectivization. These scenarios also introduce shoreside electricity in
shipping.
Data availability
The model output data are available upon request from the corresponding
authors.
Code and data availability
TAPM is a commercial software available from CSIRO, Australia
(https://www.csiro.au/, Hurley, 2008a). STEAM is the intellectual property of the
Finnish Meteorological Institute and is not publicly available. The ARP tool is intellectual property of Mike Holland and Joe Sparado (mike.holland@emrc.co.uk) and is not publicly available.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-7509-2020-supplement.
Author contributions
LT, MOPR, JM and VM designed the model simulations. LJ and JPJ calculated
ship emissions with STEAM and contributed text about the
shipping emissions. LT prepared ship emission files for the model
simulations. LT, MG and JM prepared emission data from other sources. MK and
AA prepared data from the regional-scale simulation used for the boundary
conditions, and MK contributed text about these simulations. LT and MOPR
prepared the model set-up and other input data, performed the model
simulations and evaluated the model results. LT calculated exposures, and JM
and KY calculated the health impacts. LT and JM wrote the majority of the
text, with assistance from MOPR and VM.
Competing interests
Jana Moldanová is associated editor of the special issue Shipping and Environment.
Special issue statement
This article is part of the special issue “Shipping and the Environment – From Regional to Global Perspectives (ACP/OS inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Hulda Winnes and Stefan Åström, IVL, are acknowledged for valuable
comments on the manuscript. Two anonymous reviewers are gratefully
acknowledged for valuable suggestions and comments.
Financial support
This work resulted from the BONUS SHEBA project (grant no. 2014-41), which has received funding from BONUS (Art 185), funded jointly by the EU, the Academy of Finland, the German Federal Ministry of Education and Research (under grant no. 03F0720A) and the Swedish Environmental Protection Agency, as well as from the project platform CSHIPP, which has received funding from subsidy contract C006 of Interreg Baltic Sea Region.
Review statement
This paper was edited by Huan Liu and reviewed by two anonymous referees.
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