In this paper, a methodology is presented for modelling underwater noise emissions from ships based on realistic vessel activity in the Baltic Sea region. This paper combines the Wittekind noise source model with the Ship Traffic Emission Assessment Model (STEAM) in order to produce regular updates for underwater noise from ships. This approach allows the construction of noise source maps, but requires parameters which are not commonly available from commercial ship technical databases. For this reason, alternative methods were necessary to fill in the required information. Most of the parameters needed contain information that is available during the STEAM model runs, but features describing propeller cavitation are not easily recovered for the world fleet. Baltic Sea ship activity data were used to generate noise source maps for commercial shipping. Container ships were recognized as the most significant source of underwater noise, and the significant potential for an increase in their contribution to future noise emissions was identified.
It is recognized that anthropogenic noise might have adverse effects on the
marine environment. Scientific results unequivocally suggest that animals
react to sound – sometimes with devastating results
(Rolland et al., 2012; Yang
et al., 2008), but more commonly sound gives rise to strong avoidance reactions
(Moore et al., 2012). Not all marine life is
sensitive to the same kind of noise; low-frequency shipping noise (
Predicted and actual main engine masses of 31 500 four-stroke engines. The black lines represent the range given by Watson (1998). The red line indicates the mass/power dependency used in this study for cases where engine mass could not be determined.
The primary source of underwater noise from ships is propeller cavitation. Cavitation occurs when a fast rotating propeller pushes water with its blades and a low pressure zone forms on the backside of the blade. Water boils and forms collapsing bubbles which violently burst, emitting noise in the process. All propellers cavitate when rotated fast enough, but propeller design can affect how easily this occurs. The issue with this is that efficient propulsion and the suppression of cavitation are two conflicting requirements. Currently design rules (IMO, 2014) exist regarding the energy efficiency of new ships, but no binding regulation has been put forward to mitigate underwater noise from ships (IMO, 2013). Therefore, it is easy to understand that designing an efficient propeller is more important than designing a quiet propeller, unless a low noise signature is required on the battlefield (warships), or for the purpose of not disturbing test subjects (research vessels) (Leaper et al., 2014).
Modelling underwater noise from ships has been carried out for a long period of time, and various models have been designed to describe noise sources based on measurements made since the Second World War. However, these models often rely on confidential data sets, which are not necessarily available for civilian research efforts. Nevertheless, over the last two decades significant effort has been made to generate an experimental basis for noise model development (Arveson and Vendittis, 2000; Kipple, 2002; McKenna et al., 2012; Wales and Heitmeyer, 2002). These data have been used to construct noise source models, which rely on a parametric description of ensemble source spectra for merchant vessels. Recently, Wittekind (2014) described noise sources using a method that presents ships as individual sources of noise which arise from individual technical features and vessel operation.
Automatic Identification System (AIS) data have been used to track exhaust emissions from ship traffic, but their use in underwater noise source modelling has only been the subject of few studies where they have mostly been used to locate noise sources relative to hydrophone setups (Hatch et al., 2008; McKenna et al., 2012). Our study extends on this idea and builds on the development of the Ship Traffic Emission Assessment Model (STEAM; Jalkanen et al., 2009, 2012; Johansson et al., 2013, 2017). This approach combines the vessel level technical description, an existing noise source model (Wittekind, 2014) and ship activity obtained from AIS data; furthermore, it facilitates the regular updates of noise source maps of any level, ranging from local to global, depending on the availability of AIS data. These data could be used to assess shipping noise, further the understanding of noise as an environmental stressor and provide tools for future sustainable governance of marine areas.
The aim of this paper is to (a) introduce a methodology for noise source mapping, which could be used for routine annual reporting of underwater noise emissions, (b) provide insight into the geographical distribution of vessel noise in the Baltic Sea region and (c) provide a summary of results for noise emissions from Baltic Sea shipping during 2015.
The Ship Traffic Emission Assessment Model (STEAM; Jalkanen et al., 2009, 2012; Johansson et al., 2013, 2017) was used in this study. The Wittekind noise source model (Wittekind, 2014) was built into STEAM which facilitated noise source descriptions based on the technical characteristics of individual vessels. The selection of the noise model for implementation was based on the performance of the model, the availability of the technical data required for proper implementation and separate descriptions of high- and low-frequency contributions to source levels. Furthermore, the Wittekind model is based on measurements that were made for a modern vessel fleet. Conceptual modelling using AIS to describe vessel activity and technical data to describe the vessel features is independent of the choice of the source model.
The activity data used for this study consisted of 500 million AIS position reports sent by ships sailing the Baltic Sea during the year 2015. The data were provided by the member states of the Helsinki Commission (HELCOM). STEAM uses AIS to describe vessel location, time, identity and speed over ground and combines these data with vessel technical data from IHS Fairplay (IHS_Global, 2016) and publicly available shipping data sources (classification societies, engine manufacturers). This combination allows for predictions of instantaneous engine power, fuel consumption and emissions as a function of vessel speed. Further details regarding the model can be found in a recent paper by Johansson et al. (2017).
The Wittekind noise source model describes ship noise as a combination of
three contributions, which arise from low- and high-frequency cavitation and
machinery noise. These noise sources are linked to vessel properties, such as displacement,
hull shape and machinery specifications, which is in contrast with some
previously introduced ship noise models (McKenna et al., 2012; Wales and Heitmeyer, 2002).
The cavitation contributions are dependent on vessel speed, whereas the machinery contribution is not. This has
important implications for the noise source map generation and the time
integration components of this work, which will be described in Sect. 2.6. The three components are described by
Wittekind as
Predicted and actual main engine masses of 24 000 two-stroke engines. The black lines represent the range given by Watson (1998). The red line indicates the mass / power dependency used in this study.
In Eq. (1)
In Eq. (2),
As can be seen, the Wittekind model uses parameters that are ship specific
and which lead to individual noise source descriptions depending on vessel
features; however, some of these parameters are not available from the ship databases
that provide other vessel specifications. Nevertheless, there are numerous parameters
that need to be derived during the noise source calculations. Some of
these, such as
Main engine mass is not routinely included in commercial ship databases;
therefore, we have augmented the STEAM database with engine masses obtained from
technical documentation from engine manufacturers and engine catalogues
(Barnes et al., 2005). Engine mass could be explicitly determined
for about two-thirds of the global fleet. For the third, a linear function was developed to estimate
the engine mass based on the size (installed power) of engines. For four-stroke
engines, the main engine mass was determined by multiplying the installed
kW / engine by 0.0155 which corresponds to a 65 kW t
There were about 19 600 vessels equipped with four-stroke engines in this study, the mass of
which was evaluated with the proposed power / mass methodology. The
quality of the linear fit is slightly worse for four-stroke engines
(
For two-stroke engines, the engine power output was multiplied with 0.0322 (red
line). For example, the predicted mass of the MAN B&W 10K98MC-C engine is
1725–1797 t, whereas manufacturer specifications indicate a mass of 1854 t. Watson recommends 0.035–0.045 t kW
For gas turbine machinery, 0.001 t kW
Unfortunately, an engine mounting parameter is not available in the existing technical databases. The main engines of a ship can be bolted directly to the rigid box girder without additional damping material to absorb vibrations of engines. This is known as rigid mounting and is usually applied to large two-stroke engines, but it can also be used for some large four-stroke engines. Resilient mounting of the engine, in comparison, is used if it is necessary to reduce structure-borne vibrations or noise that would otherwise be transmitted to the hull. According to Rowen (2003) and Kuiken (2008), resilient mounting is usually applied to medium- and high-speed diesels, which are sufficiently rigid with respect to bending and torsion. In this work, all two-stroke engines have been assigned a “rigid mounting” status, whilst “resilient mounting” is assumed for all four-stroke engines, although, as previously stated, some four-stroke engines can be installed using either method (Wartsila, 2012, 2015 2016). We investigated the impact of these assignments on the emitted noise levels from several kinds of ships. Source level curves for some of these cases can be found in Supplement.
The description of cavitation is, among other factors, a function of the
propeller disc area and the propeller tip speed. The commercial ship databases do
not contain enough information regarding propellers installed on ships, such as
the number of blades and diameter, to generate the cavitation inception speed. An
alternative method of determining this parameter has consequently been developed founded on
discussions with a manufacturer of propulsion equipment. Following these
discussions, an approach based on the vessel block coefficient and design speed
was developed (Eq. 5):
To represent underwater noise emissions as a map, an approach was developed
to facilitate this form of emission reporting. The source level is related
to the power emitted (
Noise source map for Baltic Sea shipping. This map indicates the sum of
sound energy in units of joules per grid cell (cell area 1 km
Ships spend a significant portion of their active time in harbour areas
(Smith et al., 2014). The time integration step in this study (Eq. 7) leads to a situation
where harbour areas are represented as significant sources of underwater
noise. This is a feature of the machinery contribution of the noise source
description (see Eq. 4) which remains non-zero
when ships are standing still. Using the current approach it is not possible
to distinguish between ships standing still with engines on or off. The
Wittekind noise source model is intended for moving vessels and the application
of this model to stationary vessels would have been a clear extrapolation of
the original intention. For this reason, we chose to only apply the time
integration of sound power to moving ships. In STEAM, time integration of
sound power is only applied to the cruising and manoeuvring modes of vessel
operation, and stationary vessels do not contribute to total sound energy
regardless of the fact that there may be auxiliary engines running during
harbour visits which may contribute to the emitted underwater noise. Noise
from auxiliary engines is not modelled in this approach even if it may be
a significant source of atmospheric noise in harbour areas. Based on these
definitions, a source emitting 1 MJ of noise in 1 year
corresponds to a continuous monopole source with an approximate 156 dB re 1
The noise maps were generated for one-third octave bands which have 63, 125 and 2000 Hz central frequencies (Van der Graaf et al., 2012). The two lowest bands are relevant for various fish species, whereas the 2 kHz band is relevant for marine mammals (Nedwell et al., 2004; Nikopouloulos et al., 2016). Using the methodology described above, the noise source maps generated for Baltic Sea shipping in 2015 (for 63 Hz band) are depicted in Fig. 3.
As can be seen from Fig. 3 noise source maps have
noise hotspots on the main shipping lane in the Danish straits, between the
islands of Fyn and Sjælland. Furthermore, high sound energy values were
estimated outside Kiel and Rostock harbours. The annual noise energy emitted in
the 63 Hz band was 117 GJ during 2015, with the highest contributions
from bulk cargo ships, container ships and tankers. Noise emissions were
also observed to increase towards the end of 2015. Maximum monthly noise energy emissions were noted in
December 2015, 32 GJ month
Plotting the noise energy emitted by each ship type, relative to the total noise
energy emitted in each band, indicates that container ships and bulk cargo
carriers are the two largest sources of underwater shipping noise in the
Baltic Sea region. Container ships represent about 3 % of all ships,
but are responsible for 27 % of the noise emitted in the 125 Hz band. Bulk
cargo carriers also contribute a high share of noise emissions, but bulk
carriers represent a larger share of the total number of ships (8 %).
(Fig. 4; Table 1). Analogous to energy efficiency metrics,
reported in grams of
For most cargo ships
Noise energy emitted by various ship types in the Baltic Sea region during the year 2015. The top 10 contributors are reported and represent over 90 % of the noise energy emitted.
Karasalo et al. (2017) tested the performance of the Wittekind noise source model using inverse modelling from hydrophone measurements. The transmission loss of the measured noise signature was modelled using XFEM code (Karasalo, 1994) to obtain the noise source at a reference distance. In their paper, Karasalo et al. (2017) observed a good fit between the Wittekind predictions and the observed signals for cargo ships, tankers and tugboats, but larger differences were observed for passenger and RoRo vessels, with the Wittekind model overestimating the noise source levels. It is very likely that this is because the Wittekind model was mainly intended for large ocean-going vessels with a single fixed pitch propeller or a single controllable pitch propeller that are operated close to their design pitch (Dietrich Wittekind, personal communication, October 2017).
The voluntary operation of a vessel at lower speeds (slow steaming) may work as a noise mitigation option for deep ocean vessels with a single fixed pitch propeller; however, this method may not be effective for ships equipped with controllable pitch (CP) propellers and may actually lead to higher than expected noise emissions (Wittekind, 2009).
Contribution of different ship types to annual emissions of underwater noise energy (share of energy emitted in the 63, 125 and 2000 Hz bands). The dark blue bar represents the share of the specific ship types with respect to all ships included in the study; orange represents the share of transport work; grey, yellow and light blue represent the share of noise energy emitted by ships in the 63, 125 and 2000 Hz bands, respectively with respect to the total energy
.
Noise energy emitted by different ship types in 125 Hz frequency band (in joules per year; blue bars, left axis). The share of the fleet operating under their cavitation inception speed is also indicated (orange bars, right axis). For example, container ships are the biggest source of noise energy in the Baltic Sea fleet with 27 GJ, of sound power emitted. Of the container ship fleet, about 20 % operate at speeds lower than their predicted cavitation inception speed.
Significant uncertainty may be involved in the estimation of the cavitation
inception speed (
The uncertainty concerning the estimation of
The Wittekind model was built for vessels with a single propeller and a four-stroke main engine. The application of the Wittekind model to large two-stroke engines, which commonly propel the global fleet, may lead to increased uncertainty in predicted source levels. Most (82 %) of the commercially operated vessels in the Baltic Sea use four-stroke engines and the great majority (90 %) is equipped with a single propeller. The Wittekind model also does not include contributions from auxiliary engines, which may be a significant noise source in port areas. This was one of the reasons that this contribution was exempted from the time integration of noise energy. Neglecting the continuous time integration during harbour visits also produces some uncertainty in the final results, but the magnitude of this contribution is difficult to estimate as the current approach is not able to distinguish between ships anchored with their engines shut down and ships anchored with their engines running. It is very likely that harbour areas are not significant fish or marine mammal habitats, which should reduce the significance of this uncertainty concerning the consequent noise impact assessments on marine life.
Underwater noise is rarely a design parameter for new ships, unless warships or research vessels are considered, and only voluntary guidelines to mitigate vessel noise exist. Currently, for the commercial fleet, the efficiency of the propeller is more important than low noise emissions, and these two conflicting requirements may lead to worse noise problems in the future when more energy efficient designs are required. Cavitation of propellers is usually avoided to alleviate mechanical problems arising from erosion, not to mitigate noise emissions.
A methodology was presented to derive underwater noise emissions from ship activity and technical data. This facilitates annual updates of noise source maps for the 63, 125 and 2000 Hz frequency bands regardless of the study scale. With global AIS data, global noise source studies are also possible.
During 2015 the most significant noise sources in the Baltic Sea were bulk carriers and container ships. Container vessels represented about 3 % of the total number of IMO registered vessels, but were responsible for one-quarter of the noise energy emitted; this makes them the largest contributor to vessel noise in the Baltic Sea region. It was discovered that about 20 % of container ships currently operate at speeds below the estimated cavitation inception speed. If these vessels were to increase their operating speed to levels closer to their design speed, a significant increase in underwater noise may occur in the Baltic Sea region without any increase in the fleet size. However, the container ship share of the total transport work is almost as large as the container ship noise contribution. Considering the distances travelled and cargo carried, RoPax vessels have a disproportionally large contribution to vessel noise. It is unclear how well the current approach can be applied in multi-propeller, multi-engine cases for which the Wittekind noise model was not originally intended. Further work is needed to understand the performance of current noise modelling tools in these cases.
It is unclear what kind of physical impact the current level of shipping noise has on marine life in the Baltic Sea. Shipping is only one source of underwater noise and many other sources exist, both natural and anthropogenic. Noise is not routinely monitored, but it is measured in many research projects concentrating on underwater noise. There are no long-term observations of noise that could be used to determine how noise levels have developed in the Baltic Sea in the past, but AIS data are available for at least the last decade. This allows for noise modelling studies covering this period. In general, modelling must rely on robust experimental data, which should be available to assess the performance of the modelling work. Currently, only limited opportunities to do this exist from a handful of research projects; therefore, national measurement networks and international cooperation are needed.
The noise source emission maps in netcdf format are available
in the Data Dryad service:
The supplement related to this article is available online at:
JPJ was responsible for the overall coordination of the work, the Wittekind noise model adaptation for STEAM and was the main contributor to this paper. LJ was responsible for the technical implementation of the noise module and for running the STEAM model. ML and RB provided technical expertise regarding the noise model selection and adaptation. PS, MÖ, IK and MA were responsible for developing a methodology for the noise source mapping and the consecutive noise propagation modelling, which contributed to the uncertainty evaluation. HP and JP provided expertise on the relevant impacts of noise on marine life and contributed to noise source mapping method development.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Shipping and the Environment – From Regional to Global Perspectives (ACP/OS inter-journal SI)”. It is a result of the Shipping and the Environment – From Regional to Global Perspectives, Gothenburg, Sweden, 23–24 October 2017.
This work resulted from the BONUS SHEBA project and was supported by BONUS (Art 185), which is jointly funded by the EU, the Academy of Finland, the Swedish Agency for Marine and Water Management, the Swedish Environmental Protection Agency and FORMAS. We are grateful to the HELCOM member states for allowing the use of HELCOM AIS data in this research. Edited by: John M. Huthnance Reviewed by: Jan Hallander, Dietrich Wittekind, Martin Gassmann and Adrian Farcas