Emissions originating from ship traffic in European sea areas were modelled
using the Ship Traffic Emission Assessment Model (STEAM), which uses
Automatic Identification System data to describe ship traffic activity. We
have estimated the emissions from ship traffic in the whole of Europe in
2011. We report the emission totals, the seasonal variation, the geographical
distribution of emissions, and their disaggregation between various ship
types and flag states. The total ship emissions of CO
The cornerstone of air quality modelling research is an up-to-date description of emissions from all sectors of anthropogenic (i.e. industry, agriculture, transport) and non-anthropogenic (i.e biogenic, desert dust, wildland fires) activities. However, information on emissions may have limited dynamical features, such as the geographical or temporal variations of emissions. This is especially important for transport emissions, which vary substantially both spatially and temporally.
Determination of shipping activity has previously been one of the largest unknowns in assessing the emissions from the maritime transport sector. The traffic activities of shipping in Europe are nowadays well known, as compared with vehicular traffic; this was not the case previously. The introduction of automatic vessel position reporting systems, such as the Automatic Identification System (AIS), have significantly reduced the uncertainty concerning ship activities and their geographical distribution. Nowadays, all vessels larger than the 300 t size limit globally report their position with a few second intervals; this has resulted in an availability of information on ship activities at an unprecedented level of detail. The ship emission inventories, which are based on such automated identification systems, have several significant advantages over the previously developed approaches. Such inventories are based on time-dependent, high-resolution dynamic traffic patterns, which can also allow for the effects of changing conditions, such as, e.g. marine and meteorological conditions (e.g. harsh winter conditions and sea ice cover) or weather routing.
Previous studies concerning the ship emissions in Europe have been based on statistics of cargo volumes (Schrooten et al., 2009), vessel arrival and/or departure times (Whall et al., 2002), voluntary weather reports from ships (ICOADS, Corbett et al., 2007) or search and rescue services (AMVER, Endresen et al., 2003; Wang et al., 2008). None of these data sources is able to reflect the total ship activity with full flexibility of traffic activity and temporal changes. Inconsistencies can exist between geographical emission inventories and satellite observations of pollutants (Vinken et al., 2014). Furthermore, important emission sources, like ships in harbours have been often neglected from regional emission studies.
The availability of the shipping activity data for research can be a challenging task; however, there are several options for data acquisition. Data collected by maritime authorities are rarely available for research purposes. However, there are networks of volunteers maintaining AIS base stations; activity data can therefore either be shared or are commercially available. Most satellite AIS data sets are available from commercial service providers, but also national space programs may provide access to these. Automatic AIS data collection facilitates annually updated ship emissions in the EU waters; however, the coverage area should be expanded to the Northeast Atlantic Ocean. This could be done with the inclusion of other activity data sources, such as, e.g. the satellite AIS data, which could be used to extend the AIS coverage, e.g. to fully cover the EMEP modelling domain.
In this work, we present emissions for European sea regions, which are covered by the terrestrial network of AIS base stations. In general, European seas are relatively densely trafficked, especially in regions in which intercontinental ship traffic intersects with busy short sea shipping routes. The vessel activity data from this area have been collected at the operational Vessel Traffic Services centre at the European Maritime Safety Agency. This centralized data archive allows one of the most comprehensive high-resolution sources of vessel activity on a continental scale. The modelling approach of the present study can be largely automated, which facilitates annual updates of large-scale ship emissions. This allows, e.g for the inclusion of the impacts of policy changes, such as sulphur reductions, to be included in the emission inventories used in air quality applications.
We have used the Ship Traffic Emission Assessment Model (Jalkanen et al., 2009, 2012; Johansson et al., 2013) which combines the vessel activity (AIS data) with vessel-specific information of main and auxiliary engines. This allows the determination of vessel-specific emissions, which are based on the detailed technical information of fuel-consuming systems onboard. Fuel type used during harbour stays or open seas will be determined from actual vessel activity and engine characteristics taking possible sulphur restrictions in specific regions into account. The fuel type assignment (residuals/distillates) is determined from technical specifications of ships' engines which can provide a more realistic description of the use of various marine fuels than fleet-wide adoption of residual fuels.
The aim of this study is to present a comprehensive inventory of ship
traffic exhaust emissions for a number of contaminants (CO
This modelling approach uses as input values the position reports generated
by the automatic identification system (AIS); this system is globally
on-board in every vessel that weighs more than 300 t. The AIS system
provides automatic updates of the vessel positions and instantaneous speeds
of ships at intervals of a few seconds. For this paper, we used the AIS
messages received by the terrestrial AIS network and provided by the
European Maritime Safety Agency (EMSA). We extracted the data that
corresponded to the year 2011; the data contained more than 10
The geographical coverage of the terrestrial AIS network in Europe. The colour scale illustrates the number of position reports per unit area, received in the EU sea areas in 2011.
Most of the European sea areas are well represented in these data. However, the Arctic Ocean has not been included. Extensive open-sea areas, such as the Atlantic Ocean, are also not completely represented, due to the limited reception range of the terrestrial AIS base station network. There are also spatial gaps of the data in the southernmost parts of the Mediterranean, especially near the northern African coastline. The data did not include position reports from any of the African countries; however, the ship activity in this area is significantly lower than in the northern parts of the Mediterranean. This was shown with an independent investigation of satellite AIS data sets obtained from the Norwegian Coastal Administration (detailed results not shown here). The data from inland waterways in Europe have been included, but cannot be taken to fully reflect the inland shipping, as the IMO SOLAS regulation does not require the use of AIS from these vessels.
The model requires as input also the detailed technical specifications of all fuel-consuming systems on-board and other relevant technical details for all the ships considered. Such technical specifications were therefore collected and archived from various sources of information; the data from IHS Fairplay (IHS, 2014) was the most significant source. The technical data were supplemented with material from several other companies and agencies. These included the following: Det Norske Veritas, Nippon Kaiji Kyokai, Bureau Veritas, Germanischer Lloyd American Bureau of Shipping, publicly available ship registers (such as the Korean, Norwegian, and Russian ship registers), ship owners and engine manufacturers. Fuel type was determined based on the properties of engines, such as power output, angular velocity and stroke type. The sulphur content was assigned based on the current regulations in European sea areas, such as the MARPOL Annex VI (IMO, 2008) and the EU Sulphur Directive.
The technical specifications were collected and archived for more than 65 000 vessels that have an International Maritime Organization (IMO) number. This set of ships represents a majority of the global commercial fleet. In addition to these vessels, the AIS position reports were received from more than 35 500 vessels, for which the technical data could not be determined based on the information from classification societies, such as the Lloyds Register. In addition to the IMO number, the vessel Maritime Mobile Service Identity (MMSI) code was used as a secondary key in searching vessel data from ship databases.
However, the vessel data were not received for a vast majority of vessels
that transmitted the MMSI code (and no IMO number) in AIS data. An
additional attempt to identify these vessels with internet search engines
using MMSI code was made for 5000 vessels, which had the largest fuel
consumption. This revealed some potentially large vessels, but the impact of
this step on overall CO
The emissions presented in this paper were evaluated using the Ship Traffic Emission Assessment Model (STEAM). A brief overview of this model is presented in the following; for a more detailed description, the reader is referred to Jalkanen et al. (2009, 2012) and Johansson et al. (2013). This study does not introduce any refinement of the model.
The STEAM model was used to combine the AIS-based information with the detailed technical knowledge of the ships. This combined information is used to predict vessel water resistance and instantaneous engine power of main and auxiliary engines. The model predicts as output both the instantaneous fuel consumption and the emissions of selected pollutants. The fuel consumption and emissions are computed separately for each vessel, by using archived regional scale AIS data results in a regional emission inventory. The STEAM emission model allows for the influences of the high-resolution travel routes and ship speeds, engine load, fuel sulfur content, multi-engine set-ups, abatement methods and the effects of waves (Jalkanen et al., 2012; Johansson et al., 2013).
The STEAM model includes a possibility to model some environmental effects on ships, such as the effects of waves and the influence of sea currents. However, for simplicity these factors were not taken into account in this study. The waves increase fuel consumption and emissions, whereas the direct effects of the wind and sea currents can be negative or positive. In considering long timescales and extensive regions, the net influences of direct wind effects and sea currents are expected to be fairly small. It would be possible also to use satellite-based AIS messages as input values of the model; however, for simplicity these were not used in this study, except for the above-mentioned confirmation of lack of significant vessel activity in the southern Mediterranean Sea.
The emissions of NO
We have included in the modelling most of the various engine setups, such as gas turbines, diesel electric and mechanical power transmission, nuclear vessels and sailboats. We allowed for the fact that the operation of a shaft generator is possible for vessels, which have been indicated to have geared drives or power take-off systems. The modelled values of engine loads also take into account multi-engine setups and load balancing of operational engines.
The STEAM model simulates the required power of the main and auxiliary engines by determining the required power level set up that corresponds to the speed value in the AIS messages. All ships are modelled individually, and the modelling takes into account the differences in hull form, propeller efficiency, shaft generators and auxiliary engine usage. The sulphur content of the fuel has been modelled explicitly for each vessel and its engines. We have allowed for the sulphur reduction techniques and the influences of the regulations regarding fuel sulphur content in various regions and during various time periods (Johansson et al., 2013).
In cases, in which more detailed information could not be obtained from
engine manufacturers, the Specific Fuel Oil Consumption (SFOC) has been
modelled based on the methods in the second IMO GHG report (Buhaug et al.,
2009). The SFOC is modelled as a function of engine load. In the model, low
engine load levels can increase SFOC up to 25 %. Operating engines outside
their optimal working range (without de-rating) will lead to increased SFOC
and emission factors. The emissions of particulate matter, sulphate and
water are modelled as a function of the fuel sulphur content. All vessels
have been treated as single displacement hulls; catamarans and hydrofoil
vessels were not separately modelled. The currently modelled pollutants are
NO
Emissions and shipping statistics in the SafeSeaNet area in 2011.
The section “All ships” includes also emissions from unidentified vessels.
“IMO registered” refers to commercial ships with specified IMO number. In
the section “GT”, eight vessel size categories and their contributions to
emitted CO
We have evaluated in more detail the emissions from locations with an
especially high emission intensity, which we refer to as shipping emission
“hotspots”. The STEAM model has been executed on a resolution of
approximately 2.5 km The sum of emissions in the vicinity of each grid cell has been calculated
within a radius of 10 km (such a domain contains approximately 44 closest
grid cells). The sum (if high enough) along with centre coordinates are placed in the
list of top 30 highest ranking CO The first and second steps are repeated until each cell in the emission grid
has been once the candidate emission hotspot.
This analysis also indicates the areas with the highest ship fuel consumption, whether this occurs in harbour areas or along shipping lanes.
Predicted geographic distribution of shipping emissions of
CO
A compilation of computed emissions, payloads, numbers of ships, and
distances travelled has been presented in Table 1. The geographical
distribution of ship CO
The highest CO
The international cargo traffic contributes significantly to the emissions at the most densely trafficked shipping lanes; a prominent example is the ship route in the Mediterranean Sea that extends from Suez Canal to Gibraltar. The route patterns of passenger traffic are different; these occur more frequently via shorter routes. For example, there are a lot of routes between the islands in Greece and the mainland, and between Italy and the islands of Sardinia, Corsica, and Sicily. There is a dense network of shorter passenger vessel routes in numerous sea regions in the Mediterranean. The routes of cargo and passenger traffic intersect also in several regions of the Baltic Sea and the North Sea. For example, in the English Channel passenger traffic takes place mainly across the channel, whereas most of the cargo routes are aligned along the Channel.
The 30 locations in which there were highest ship emissions of
CO
We have also analysed the areas that have the highest CO
The area including the Netherlands and the English Channel has the highest
density of these hot spots; there are in total 10 domains in these regions
amongst the top 30 shipping CO
In some sea regions, busy shipping traffic is focused in geographical
bottlenecks with high CO
Emissions of CO
The fractions of shipping emissions for European sea regions in 2011.
The locations in European sea areas that contain the highest
CO
The emissions originated from the other sea areas except for the three
specifically mentioned three sea regions (Baltic Sea, North Sea and
Mediterranean Sea) have also been reported in Table 1 and
Fig. 4. These areas include the western parts of
the Black Sea, Canary Islands, Celtic Sea, Barents Sea and Northeast
Atlantic Ocean. The emissions from shipping in these other regions were
estimated to produce almost one quarter of CO
These results have obvious policy implications. Reductions of ship exhaust emissions in areas with high emission levels and a surrounding dense population is likely to yield major health benefits (e.g. Corbett et al., 2007; USEPA, 2008; Bosch et al., 2009; Brandt et al., 2013; Jonson et al., 2015). However, policy changes for reducing shipping emissions may have significant cost impacts (e.g. Johansson et al., 2013; Kalli et al., 2013), which necessitates thorough assessments of both the costs and the benefits. The identified emission hot spots, especially those which are in the vicinity of major cities, are prime candidates for enhanced emission control measures. The low fuel sulphur requirement of the EU directive has already addressed some aspects of this issue.
The AIS signals include a Maritime Mobile Service Identity (MMSI) code that contains information that specifies the flag state of the ship. We have selected 16 flag states that had the highest total fuel consumption in Europe in 2011, and evaluated their annual statistics of the numbers of ships, payload, and the emissions of three pollutants. The results of this analysis are included in Fig. 5. The emissions have been presented as fractions (%) of the total emissions in the European sea areas in Fig. 5.
Relative contributions of various flag states to selected
emissions, the numbers of ships and cargo payload in Europe in 2011. We have
selected 20 states that had the highest emissions of CO
The emissions were largest for the Liberian and second largest for the
Italian fleet. The UK, Malta, Bahamas, Norway, and the Netherlands also have
had major fleets. In addition to major European states, such as Italy, UK,
Norway, the Netherlands, Greece, Germany, etc., major fleets have also sailed
under the flags of relatively smaller states, such as Liberia, Malta,
Bahamas, Marshall Islands, etc. The flags of convenience allow open vessel
registration regardless of the owner's nationality (ITF,
We have allocated the emissions to IMO registered (referred here also as
“large”) and unidentified (referred to as “small”) ships in Table 1, as the
IMO registered ships constitute most of the commercial marine traffic.
According to the values in Table 1, the contribution of unidentified vessels
is only 1.7 % of the total CO
The descriptions of the technical details for small vessels in the emission inventory are limited. These are significantly less accurate than the corresponding descriptions for large vessels, for which the engine setup and technical data are readily available. Model results for the fuel consumption of small vessels are further complicated by an incomplete inclusion of the activities of small vessels; a fraction of the small vessels do not carry AIS equipment on board.
The shares of emissions for various ship types have been presented in Fig. 6. A
comparison of CO
The fractions of European shipping emissions and payload, classified in terms of the ship types, in 2011.
There were clear seasonal variations in the emissions of all pollutants; the
variations in case of CO
A disaggregated compilation of vessel types and their operational features has been presented in Table 3. The five more general level categories (cargo, container, tanker, passenger, and other) have been divided into more detailed categories. The division of vessel activity to operational modes (cruising, maneuvering, and hotelling) has not been predetermined; it has been defined by vessel activity data. Based on AIS data, it is possible to determine these explicitly, which will significantly decrease the large uncertainties that have previously been associated with vessel activities.
Seasonal variation of the shipping emissions of CO
The shares of fuel used by the main engines have also been presented in
Table 3, these have also been evaluated by the model. The amounts of fuel
used in main and auxiliary engines depend not only on vessel specifics, but
also its operational profile. However, there is a major uncertainty in the
predictions of the fuel consumption of the auxiliary engine, as the use of
an auxiliary engine varies greatly, even for ships of the same type. The use
of auxiliary power cannot be determined from tank tests of ship resistance,
unlike the power needed for propulsion, for which various theories exist for
performance prediction. In this study, we have used the methodology
presented previously (Jalkanen et al., 2009, 2012; Johansson et al., 2013).
This method combines the information on cargo capacity, auxiliary engine
power profiles, main and auxiliary engine setup and power transmission
method. However, there are also other modelling approaches, which are based
on extensive vessel boarding programs (Starcrest, 2013), local knowledge, and
pre-assigned contributions (Dalsøren et al., 2009). The share of auxiliary engine
fuel consumption from total consumption is very high for service vessels and
tugboats. This is consistent with the 2nd IMO GHG report by Buhaug et al. (2009); however, the contribution of these vessels to the total fuel
consumption or CO
Summary of average operational features of some selected ship
types. The first column indicates the aggregated ship type, whereas the
second column contains a more detailed description of vessel type. The time
spent in each operation mode (cruising, maneuvering, hotelling) is indicated
by the next three columns as percentages. “ME of Fuel” refers to the
fraction of fuel used in main engines from total fuel consumption. Cruising
speed indicates average cruising speed observed in AIS data. The last column
on the right-hand side indicates the significance of contribution to overall
CO
The comparison of the numerical results of various European-scale emission inventories can be challenging, as pointed out, e.g. by EEA (2013). The main reasons for this are that the methodologies and various modelling selections used for evaluating shipping emissions vary substantially in various published studies. E.g. the various studies may define differently the geographical domain, and some studies address only international ship traffic.
The current work reports emissions for the year 2011. Significant reductions
were therefore in force regarding the sulphur content of marine fuel in the
North Sea and the Baltic Sea area, as well as the requirement for low
sulphur fuel in EU harbour areas. The effects of these regulations were
included in the current work, and it is therefore not possible to directly
compare the predicted SO
The emissions of NO
The reported total NO
The annual SO
The reasons for such major differences in the predictions of these two inventories could be caused, for example, by the neglect of the impacts of relevant legislation, such as the EU sulphur directive (EU, 2012). This directive limits the sulphur content of marine fuels to 0.1 % (by mass) in harbour areas and to 1.5 % (by mass) for passenger vessels on a regular schedule. It is possible that not all passenger ships comply with the requirement of 1.5 % fuel sulphur content, as assumed in the STEAM model. However, a possible non-compliance by a fairly small fraction of ships would explain only a minor portion of the differences between the STEAM and EMEP inventories. More information on the compliance with EU regulations can be obtained either during Port State Control checks, or via relevant compliance monitoring schemes (Balzani et al., 2014; Berg et al., 2012; Beecken et al., 2014, 2015; Pirjola et al., 2014).
Plotting the EMEP time series of SO
It is not possible to perform a similar satellite-based comparison for
SO
The inventory of Cofala et al. (2007) includes an estimate for ship CO
The differences in PM
The range of European shipping emissions of CO
The comparison of emitted pollutants with existing ship emission inventories
revealed that there are some differences between the estimates of the
various inventories for the emissions of ships sailing the Mediterranean
Sea, whereas the results were better in agreement for the North Sea and the
Baltic Sea regions. The NO
Further research is required including emission modelling in combination
with consecutive chemical transport modelling, comparisons with measured
atmospheric concentrations of pollutants and source apportionment. The
reasons for these deviations between different emission inventories should
be investigated further and confirmed with independent experimental
data sets, as these can have significant policy implications concerning
health and environmental impact assessments within the transport sector. A
logical step would be to include chemical transport modelling and comparisons
with air quality measurements especially at coastal stations to determine
whether the predicted NO
Despite the wide geographical extent, the ship emission data can also be segmented in terms of the various properties of vessel categories or individual vessels. This makes it possible to classify the emissions using several criteria. The disaggregation of ship emissions into individual vessels on a fine temporal resolution also allows fine resolution air quality and health impact assessment studies. A specific advantage of an inventory based on individual vessel data is that it facilitates comparisons with experimental stack measurements.
According to this study, the vessels carrying an EU flag were responsible
for 55 % of CO
The emissions from ships have a clear seasonal variation; the emission maximum occurs during the summer months. This concerns especially passenger traffic, but also containerships have the same seasonal pattern. However, the emissions originated from oil tankers and other cargo ships do not have a clear seasonality. Temporal variation of ship emissions has mostly been neglected in previous emission inventories, due to inherent limitations of the activity data used as a basis for these inventories. Seasonal variations can be of the order of 30 %; these features should therefore be included in emission and health impact assessments.
The current work also facilitates studies of ship energy efficiency, as all emissions and fuel data are generated on the ship level. There were substantial differences between fuel burned and transport work carried out by various ship types. The unit emissions were the lowest for the oil tankers and largest for passenger vessels. However, the description of transport work of passenger vessels currently considers cargo operations and does not completely cover passenger cargoes.
The gridded emission data sets of this work can be made available for further research upon request to the authors.
This study was made possible as a result of cooperation with the European Maritime Safety Agency and the Norwegian Coastal Administration. We would like to thank both agencies for making the relevant AIS data sets available for this work. We are also grateful for financial support of the European Space Agency (Samba project), FP7 project TRANSPHORM and the Academy of Finland (APTA project). This work is partly based on the material supplied by IHS Fairplay. Edited by: W. Birmili