Ship Track Data Research Articles

Climate and air quality impact of using ammonia as an alternative shipping fuel

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO x , NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO x , NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NO x , low NH3 emissions) vs pure NH3 (low NO x , very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16 900 mortalities from PM2.5 and 16 200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr−1) from pure NH3 engines could lead to 668 100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current emission control areas leads to smaller improvements in PM2.5-related mortalities (22 100 avoided mortalities for NH3–H2 and 623 900 additional mortalities for pure NH3 annually), while extending both Tier III NO x standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH3–H2 and 1200 additional mortalities for pure NH3 annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

Mapping past and future shipping noise in European seas

Against the backdrop of a steadily increasing demand for sea transport of goods and people, the development of a reliable marine shipping soundscape model is an essential planning requirement to assess the effect on ocean noise of operational and technological changes aimed at mitigating the environmental impact of the shipping sector. The NAVISON (Navis Sonus) project, conducted with the support of the European Maritime Safety Agency, employs a specially developed parametric vessel source model with the objective of producing shipping sound maps in European seas for past, present, and potential future conditions over a time span from 2016 to 2050. The source model is combined with historical ship tracking data from the automated identification system (AIS), or projected shipping densities and mitigation scenarios, to calculate spatial ship noise emissions data for input to a sound mapping tool. The mapping tool computes underwater sound propagation using the parabolic-equation method, drawing upon ocean-scale databases of bathymetric, oceanographic, and sediment properties. Project outputs are provided as map layers of sound pressure level and sound energy according to vessel type, season, region, year, and operational conditions; from these layers, maps can be generated for user-specified combinations of mitigation measures. Maps are presented in two frequency bands (centred at 63 Hz and 125 Hz) selected for assessing Good Environmental Status in the context of the European Union’s Marine Strategy Framework Directive.

Deep Learning Aerosol-Cloud Interactions from Satellite Imagery

Satellite imagery can detect a wealth of ship tracks, temporary cloud trails created via cloud seeding by the emitted aerosols of large ships, a phenomenon that cannot be directly reproduced by global climate models. Ship tracks are satellite-observable examples of aerosol-cloud interactions, processes that constitute the largest uncertainty in climate forcing predictions, and when observed are also examples of Marine Cloud Brightening (MCB), a potential climate intervention strategy. Leveraging the large amount of observed ship track data to enhance understanding of aerosol-cloud interactions and the potentials of MCB is hindered by the computational infeasiblity of characterization from expensive physical models. In this paper, we focus on utilizing a cheaper physics-informed advection-diffusion surrogate to accurately emulate ship track behavior. As an indication of aerosol-cloud interaction behavior, we focus on learning the spreading behavior of ship tracks, neatly encoded in the emulator's spatio-temporal diffusion field. We train a convolutional LSTM to accurately learn the spreading behavior of simulated and satellite-masked ship tracks and discuss its potential in larger scale studies.

An anomaly detection method based on ship behavior trajectory

An anomalous trajectory detection method based on ship trajectory clustering and prediction is proposed. The method consists of two modules, namely, trajectory clustering based on improved DBSCAN and Trajectory prediction by ProbSparse Attention-based Transformer. we propose the concept of ship behavior similarity for the first time in this paper, by decomposing the velocity according to the ship's heading to get ‘velocity coordinates' to replace latitude and longitude coordinates, on the basis of which the ship behavior trajectories are clustered and the core trajectories are extracted. By comparing the behavioral similarity between the predicted trajectory and the core trajectory with the preset threshold, it is judged whether the abnormal trajectory or the ship has abnormal operation. AF-DP is used in this method for compression of trajectory data, and Fast-DTW is used to calculate the behavioral similarity between trajectories. Notably, this is the first application of ProbSparse Attention-based Transformer in the maritime domain. The proposed method is applicable to inland river traffic near Wusongkou, Shanghai. The final experimental results show that the method we proposed can effectively identify the abnormal behaviors in the trajectory, and can provide technical support for the analysis of ship track data and the risk management of maritime transport in this region.

A Proposed New Application of Ais Ship Tracking Data for Emergency Evacuation Planning for Coastal Communities a Case of Newfoundland Island in Canada

A Proposed New Application of Ais Ship Tracking Data for Emergency Evacuation Planning for Coastal Communities a Case of Newfoundland Island in Canada

A Proposed New Application of Ais Ship Tracking Data for Emergency Evacuation Planning for Coastal Communities a Case of Newfoundland Island in Canada

A Proposed New Application of Ais Ship Tracking Data for Emergency Evacuation Planning for Coastal Communities a Case of Newfoundland Island in Canada

The Arctic underwater soundscape today and as projected for 2030

Canada, the United States, and the World Wildlife Fund are co-sponsoring ongoing work in the Arctic Council’s Protection for the Marine Environment Working Group to evaluate shipping noise in the Arctic region. Applied Ocean Sciences has used ship tracking and sea ice data to model the region’s underwater soundscape to improve understanding of radiated noise generated by shipping throughout the PanArctic. Current (2019) models have been compared with ambient noise measurements collected during time periods when vessel sounds were identifiably present and when biological sounds were not. Projections of ice cover and shipping routes along and between the northern borders of Arctic countries were used to forecast potential future (2030) Arctic soundscapes. Focused interpretation of these model results within sub-regions, time periods, and frequencies important to marine fauna and in turn to indigenous peoples will be provided to PAME and other fora seeking to guide the development of shipping practices and mitigation strategies. The final results will be incorporated as a PAME/Arctic Council product. This presentation will focus on the acoustic modeling work under projected sea ice conditions, maps of “excess noise” induced by ships in 2019 and 2030, and risk assessment for a few endemic marine mammal species.

The effect of COVID-19 on underwater sound

The sounds of the turning machinery and propulsion systems from commercial ships are ubiquitous in the temperate deep ocean basins. The vertical structure of seawater temperature, salinity, and pressure conspire to create nearly lossless underwater propagation conditions at 10’s and 100’s of Hz, the frequency band where diesel engines and generators, cavitating propellers, and turning gearboxes produce their peak acoustic energy. In the shallow waters of the continental margins, the human use of the ocean for transport, fishing, recreation, and construction has led to increasing levels of anthropogenic noise over the last century. As the COVID-19 pandemic spread across the globe, the reduction of economic activity and marine traffic resulted in a decrease in underwater noise, with observations made at a number of underwater listening stations and autonomous recording devices across the ocean basins. These measurements, along with ship track data provided by the automated identification system (AIS) and trade and shipping statistics, improve our knowledge of the relationship between acoustic energy, the marine acoustic habitat, and the various human uses of the ocean.

Modelling the global soundscape: Validating a study of the COVID-19 impact using in-shore and off-shore observatory data

This study presents a data-model comparison of the soundscape in the Northeast Pacific Ocean in 2019 and 2020. Focussing on frequencies below 400 Hz, where the soundscape is dominated by shipping, marine mammals (at selected frequencies) and large storms, a parabolic-equation model computes the propagation loss from a grid of points to a four-dimensional, high-resolution grid of receivers. The modelling data are extracted from a dynamic model of the global shipping and wind noise soundscape that utilizes AIS ship-tracking data. A comparison of the modelling results between 2019 and 2020 revealed a marked difference, reasonably due to the COVID-19 global shutdown. These results are compared to measurements from the long-term time series collected by Ocean Networks Canada’s in-shore and off-shore cabled observatories in the Salish Sea and Northeast Pacific Ocean.

Aerosol-cloud-climate cooling overestimated by ship-track data.

The effect of anthropogenic aerosol on the reflectivity of stratocumulus cloud decks through changes in cloud amount is a major uncertainty in climate projections. In frequently occurring nonprecipitating stratocumulus, cloud amount can decrease through aerosol-enhanced cloud-top mixing. The climatological relevance of this effect is debated because ship exhaust only marginally reduces stratocumulus amount. By comparing detailed numerical simulations with satellite analyses, we show that ship-track studies cannot be generalized to estimate the climatological forcing of anthropogenic aerosol. The ship track-derived sensitivity of the radiative effect of nonprecipitating stratocumulus to aerosol overestimates their cooling effect by up to 200%. The offsetting warming effect of decreasing stratocumulus amount needs to be taken into account if we are to constrain the cloud-mediated radiative forcing of anthropogenic aerosol.

The value of meteorological data in marine risk assessment

The objective of this paper is to investigate whether the use of meteorological data improves the prediction of marine incidents, as represented by marine insurance claims for a vessel's voyage, both on a stand-alone basis and when combined with vessel-specific features and ship tracking data from the Automated Identification System (AIS). Furthermore, the paper investigates whether predictive performance improves when using machine learning algorithms, such as logistic LASSO regression and eXtreme Gradient Boosted Trees over classical logistic models, and identify dependencies and interaction effects among the risk factors within the SHapley Additive exPlanations framework. The data sample includes weather and AIS data for 42,000 voyages in the North Pacific between January 2013 and August 2019. The results suggest that meteorological information adds value in claims prediction and that short-term complex interactions between the vessel and weather conditions impact marine risk. The research is important for the improved modelling of marine risk on the basis of high-frequency, high-resolution ship tracking and weather data.

The CO2 reduction potential of shore-side electricity in Europe

Shore-side electricity can drastically reduce the emissions from fossil fuel-powered auxiliary engines of ships at berth. Data scarcity on the auxiliary power demand at berth has limited the scope and temporal resolution of previous studies to few ports and ships. We establish a novel method to estimate the auxiliary power demand at berth for 714 major ports in the European Economic Area (EEA) and the United Kingdom (UK). Therefore, emission report data from the Monitoring, Reporting and Verification scheme of the European Union and ship tracking data from the Automatic Identification System are combined.Annual emissions of 3 Mt (/ 5 Mt) CO2 could be avoided if the auxiliary power demand at berth would be supplied from national grids (/ from CO2-neutral electricity). This equals an average reduction of overall shipping emissions by 2.2% (3.7%), and requires only 0.2% (6.4 TWh) of the current electricity generation capacity of the EEA and the UK. Using shore-side electricity from the grid can also contribute to substantial annual local air pollution reductions of 86,431 t NOx, 4,130 t SOx, 1,596 t PM10, 4,333 t CO, 94 t CH4, 4,818 t NMVOC, and 235 t N2O.

Improving disasters preparedness and response for coastal communities using AIS ship tracking data

Many coastal communities are heavily dependent on maritime transportation for the ingress and egress of people and goods. Any major transportation disruption can have a significant negative impact on the safety, health and wellbeing of affected communities, this is due to the interruption in the availability of food and the supply medicines and fuel. Therefore, preparedness and the forward planning of an effective response are essential for successful emergency and recovery management. Accordingly, in this study, the concept of using AIS (Automatic Identification System) vessel tracking data has been applied for the study of disaster management in coastal communities. The AIS vessel tracking system has been an important development in navigational safety; this is because it continuously transmits important information to all other vessels about a particular vessel (including its position, identity, speed and route). One of the limitations of the AIS tracking system is that AIS data does not indicate commodity specifications: that is, the quantity of essential goods that each vessel is carrying to specified coastal communities. To overcome the limitation of the current AIS tracking system, we use an artificial neural network as an estimation tool. In the current study, AIS data are assessed and analyzed in addition to the augmentation of the capacity information of vessels; thus, the study develops a predictive model so that a relief manager can determine the actual needs of affected residents and thus be able to make responsible relief decisions (e.g., how much relief a disaster-affected community is likely to need). The study makes a unique contribution as its focus seeks to remedy the total lack of research on how to use AIS data in disaster operations.

Validated shipping noise maps of the Northeast Atlantic

Underwater noise pollution from shipping is globally pervasive and has a range of adverse impacts on species which depend on sound, including marine mammals, sea turtles, fish, and many invertebrates. International bodies including United Nations agencies, the Arctic Council, and the European Union are beginning to address the issue at the policy level, but better evidence is needed to map levels of underwater noise pollution and the potential benefits of management measures such as ship-quieting regulations. Crucially, corroboration of noise maps with field measurements is presently lacking, which undermines confidence in their application to policymaking. We construct a computational model of underwater noise levels in the Northeast Atlantic using Automatic Identification System (AIS) ship-tracking data, wind speed data, and other environmental parameters, and validate this model against field measurements at 4 sites in the North Sea. Overall, model predictions of the median sound level were within ±3 dB for 93% of the field measurements for one-third octave frequency bands in the range 125 Hz–5 kHz. Areas with median noise levels exceeding 120 dB re 1 μPa and 20 dB above modelled natural background sound were predicted to occur in the Dover Strait, the Norwegian trench, near to several major ports, and around offshore infrastructure sites in the North Sea. To our knowledge, this is the first study to quantitatively validate large-scale modelled noise maps with field measurements at multiple sites. Further validation will increase confidence in deeper waters and during winter months. Our results highlight areas where anthropogenic pressure from shipping noise is greatest and will inform the management of shipping noise in the Northeast Atlantic. The good agreement between measurements and model gives confidence that models of shipping noise can be used to inform future policy and management decisions to address shipping noise pollution.

Crude oil contango arbitrage and the floating storage decision

We investigate charterers’ ability and willingness to exploit floating storage arbitrage opportunities. Using time-series and fixtures data on time-charter rates, we find that arbitrage opportunities were present during the Financial Crisis and the recent Oil Glut. An investigation of storage profits across storage horizons suggests that positive oil supply shocks favour longer storage horizons than negative oil demand shocks. Evidence from spatial ship-tracking data suggests charterers are reluctant to exploit the arbitrage opportunity when the implied value of transportation from Forward Freight Agreements (FFAs) exceeds the storage profit. Our findings are of interest to maritime economists and oil market participants.

Noise directionality estimated using ship tracking data in the Southern Sea of Korea

The noise directionality received by an array sensor depends on the spatiotemporal location and distribution of each noise source. Such noise directionality acts as a major factor in influencing the array gain, which determines the ability of the array sensor to detect an underwater target. Thus, the performance of the array sensor can be predicted by estimating the noise directionality. In this study, a modeling method was used to estimate the directionality of ship noise. To verify this method, the results were analyzed by comparing the noise directionality modeled using ship information received from an automatic identification system and the noise directionality measured with a horizontal line array. Although the modeled noise and measured noise directionalities were not perfectly matched, it was confirmed that the peak level and pattern shape of the directional noise pattern are similar. The factors that cause the difference between the two results are (1) the ship source level difference between the actual ship and the model, (2) the impact of ships not using the Automatic Identification System (AIS) as mandatory, and (3) the noise generated by wind, marine organisms, and fluid motion. If these factors are reflected in the noise directionality model using the ship information, better results will be obtained.

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.

Exploring ship traffic variability off California

Seaborne trade continues to grow and is an important component of the global economy. Threats from shipping to marine ecosystems include oil spills and other water pollution, air pollution, anchor scouring, biological invasions, container loss, chronic noise, and collisions between ships and large whales. Shipping and its associated threats can be influenced by a suite of regulations and economic events. The dynamic nature of ship traffic can be characterized using ship tracking data from automatic identification system (AIS) technology. These data enhance our ability to analyze the ecological threats from commercial shipping as a component of spatially explicit risk assessments. We explore ship traffic variability using a case study in waters off California. AIS data from 2008 to 2015 were used to evaluate the role of vessel emission regulations and economic events on vessel routes and speeds. We document vessels navigating around emission control areas (ECAs) or reducing speed when traveling through them. Large freight vessels decreased speeds from 2008 to 2015 by about 3–6 knots in many areas, with lowered speeds observed in areas of both heavy and sparse vessel use. The timing and location of the speed reductions appear to be most influenced by state and international clean fuel standards, which required the use of more costly fuels. Therefore, the speed reductions may have provided a more cost-effective means of travel. We also found temporary speed increases off southern California when vessels used longer routes to avoid traveling through an ECA. We conclude that the establishment of ECAs had a profound influence on vessel routes and speeds, likely due to the higher costs of clean fuels. Proposals have come before the International Maritime Organization (IMO) to establish clean fuel requirements in various locations around the world to reduce air-borne emissions from vessels. Our research suggests such proposals, or other events that may affect marine fuel prices, can have key impacts on vessel behavior. Consequently, it is important to consider this variability when designing strategies to minimize threats from shipping to vulnerable biophysical systems.

Mapping change in bottom trawling activity in the Mediterranean Sea through AIS data

A growing concern about the depletion of marine resources due to fishing overexploitation and degradation of ecosystems has been demonstrated over the last decade. Monitoring the spatial and temporal distribution of fishing activities is an important tool for fisheries management which can also be used by other sectors such as fisheries science, public authorities, policy-makers and marine spatial planning. In this paper we introduce the first map of fishing activity at a Mediterranean scale of EU and non-EU fishing vessels, extracted using Automatic Identification System ship tracking data. Fishing activity maps were produced for three different years with a spatial resolution of 0.01° × 0.01°. As a main result, for the first time, changes of bottom trawl fishing activities between two consecutive years were map for the whole Mediterranean Sea. The results confirmed the suitability of this monitoring system to obtain reliable information on the extent of bottom trawl fishing activities.

New constraints on the age and style of continental breakup in the South Atlantic from magnetic anomaly data

We present new constraints on the opening of the South Atlantic Ocean from a joint interpretation of marine magnetic anomaly grids and forward modelling of conjugate profiles. We use 45,000 km of recently collected commercial ship track data combined with 561,000 km of publically available data. The new data cover the critical ocean–continental transition zones and allow us to identify and downgrade some poorly navigated older ship tracks relied upon in earlier compilations. Within the final grids the mean cross-over error is 14 nT computed from 8,227 ship track intersections. The forward modelling used uniformly magnetised bodies whose shapes were constrained from coincident deep-seismic reflection data. We find the oldest magnetic anomalies to date from M10r (134.2 Ma, late Valanginian) north of the Falkland-Agulhas Fracture Zone and M3 (129.3 Ma, Barremian) south of the Rio Grande Fracture Zone. Hence, assuming the GPTS used is correct, continental breakup was contemporaneous with the Parana and Etendeka continental flood basalts. Many of the landward linear anomalies overlap seismically mapped Seaward Dipping Reflectors (SDRs). We interpret this to mean that a significant portion of the SDRs overlay crust formed by subaerial seafloor spreading. Here crustal accretion is envisaged to be similar to that at mid-ocean ridges, but sheet lava flows (that later form the SDRs) rather than pillow basalts form the extrusive component. Segmentation of the linear anomalies generated implies that this stage of continental breakup is organised and parallels the seafloor spreading centre that follows. Our results call into question the common assumption that at volcanic continental margins the first linear magnetic anomalies represent the start of conventional (submarine) oceanic crustal generation.