Estimate vessel emissions in ports using AIS data

Abstract

The shipping industry is responsible for almost 3% of the world's greenhouse gas emissions and is looking for possibilities to reduce their share. The demand for lowering the emissions of the maritime industry creates the need for insight in the emissions. Currently a generic method to specify emissions in space and time, as a function of vessel and waterway properties is not available.
Although most of the emissions of vessels take place at sea, the most noticeable part takes place in ports since they are located close to urbanized areas. Therefore, this research focuses specifically on emissions in ports. The study investigates the impact of sea-going vessels. By gaining insight in the emissions of sea-going vessels, the big polluters can be tackled. Due to the scope of the research, not all vessel types and emission types are taken into account. Ten vessel types are selected and only CO2, NOx, SOx and PM10 emissions are estimated. These are the most relevant polluters in the shipping industry since they cause health and environmental related issues, both locally as more widely.
To provide insight in the emissions, the objective of this study is to develop a generalized method to calculate and map the emission of a single vessel in space and time in ports based on reliable data. To reduce these emissions, emission reduction strategies have to be drawn up, targeting the largest emission sources and the most crucial locations. Therefore, the developed method must not only quantify the emission sources, but must also provide insight in emission patterns in ports and indicate emission hotspots.
A bottom-up method is developed to estimate the emission of a single vessel in space and time. To derive the emission rate of a vessel, the fuel and energy consumption of the vessel is multiplied by a vessel-specific emission factor. The CO2 and SOx emissions follow a fuel-based approach, in which the emissions produced are directly proportional to the fuel consumption and therefore depend on the engine load. The energy-based approach is used for estimating NOx and PM10 emissions, which cannot be directly related to the fuel consumption but depend on engine characteristics. The fuel consumption is determined by multiplying the energy consumption of the engine with a fuel consumption factor specific to each vessel, meaning the fuel consumption is related to the energy consumption.
The amount of energy the main engines of a vessel consume, is the energy needed to overcome the resistance a ship experiences from sailing through the water and is therefore related to the vessel's speed. This speed is derived from AIS data. AIS data represents real-life vessel tracking data and is gathered automatically, which makes it a reliable and realistic data source. It also provides the ability to make the emission estimations time dependent. Due to the global coverage, AIS data is a good data source to develop a generic method applicable to ports all around the world.

However, in ports the vessel's speed alone is not a good indicator of the energy consumption, since this neglects the amount of energy the auxiliary engines consume. Their energy consumption is dependent on how much energy the electrical systems of a vessel require at that moment, which can be high when a vessel is for example at berth or manoeuvring. The energy consumption is therefore related to the operations a vessel performs. This leads to an approach which takes into account the vessel's resistance and operational modes.
Four operational modes are important to distinguish in ports: 'sailing', 'manoeuvring', 'anchoring' and 'berthing'. According to the operational mode, the main engine power is either estimated with the resistance calculation from Holtrop and Mennen when the vessel is sailing or manoeuvring, or assumed zero when laying still at anchor or berth. According to the operational mode, the auxiliary engine power can be derived from values of the IMO fourth GHG research. Depending on the emission type and vessel characteristics, the emission factor is determined.
The algorithm to determine the emission of a single vessel in space and time is implemented in a model. The model's input consists of AIS data providing the speed and position of the vessel, a vessel database containing vessel characteristics based on information from the Sea-web Ships database, and a FISgraph of the port network. This graph contains the fairway characteristics needed to calculate the resistance. The calculated emissions will also be displayed on the FIS graph for a detailed insight in the emission distribution in space.
The model provides an insight in the emissions patterns in a port. The fairway sections which are subjected to high emissions can be identified immediately and so the emission hotspots are determined. The source of the emissions can be identified by down-drilling of the emissions. The model can drill down to vessel types, operational modes and all the way down to a single vessel in space and time.
The model is illustrated by means of two case studies concerning the Port of Rotterdam and the Port of Constanța. These case studies indicate that the port basins hosting the largest vessels have the highest estimated emissions. These basins are indicated as an emission hotspot by the model when the emissions are projected on the FIS graph. In ports generally, high emissions are observed at places with a high traffic intensity, such as the port entrance. Junctions of fairway sections or port basin entrances also show locally higher emissions. The rise in emissions, is probably due to the fact that vessels are slowing down when approaching a junction. This increases the emission rate of a vessel due to more inefficient engine use, but also since they spend a larger amount of time at this fairway section.
By indicating the location and source of the emission hotspots, targeted emission reduction measures can be taken. Three of these measures are demonstrated on one of the case studies. The first strategy concerns installing shore power. The model is able to simulate vessels connected to shore power, by setting their emission rate at berth to zero. This simulation is compared to the original situation without using shore power. Out of the evaluated reduction measures, this seems the best strategy to reduce emissions since it shows the largest reduction of the total emissions. Besides that, it is an effective measure especially for ports, since the largest emissions reduction takes place at berth. The second evaluated strategy shows also good results and is about switching from a normal tugboat fleet to a zero-emission tugboat fleet. The model simulates this switch by eliminating all the tugboats from the fleet since their emissions will be zero. This new case is compared to the original situation with normal tugboats. The third option to reduce emissions is applying ECA limits which do not seem to have a lot of effect on reducing emissions except for the SOx emissions. This is derived from comparing the original situation without ECA limits, to a new case study where a situation with ECA limits is simulated. This means that the fuel types of the vessels are changed from the most economical fuel type to the lightest fuel type on board and that the sulphur content in the fuel is altered.
However, the model has some limitations. The method does not take into account the effect of currents or variations in time of the water depth. The accuracy of the resistance calculation can be improved by adding these. Furthermore, the emission pattern of tugboats needs further examination as it could not be demonstrated that the split of emissions into operational modes is correctly. The research has also shown that the method to estimate the energy consumption is not suitable for tankers at berth, since their energy consumption pattern is different. The quantity of emissions in the case study of the Port of Rotterdam is far from the expected amount of emissions. The quantification of emissions is assumed to be unreliable and further research should focus on validating these results.
Concluding, the developed model makes use of AIS data, local waterway properties, empirical emission factors and operational modes. This data is used in a physics-based method to estimate the resistance and the energy consumption. If this information is available, all this combined makes the approach in principle applicable to any port. The developed model provides an insight in the emission distribution patterns and provides the ability for down-drilling to find the source of the emission hotspots. A targeted emission reduction strategy can be proposed as a result of this and the model has the ability to evaluate specific emission reduction strategies. The strategies can be simulated with the developed model and the effect of these measures can be quantified by comparing the emission reduction strategy to a situation without these measures.