Collaborative project – MAREMIS

The MAREMIS project is targeting air pollution in harbor cities through AI-based calculation of air pollution emitted by ships and its dispersion. Singapore and Hamburg are among the largest ports in the world. The tens of thousands of ships each year are the heartbeat of the cities and the economy. Both ports are very close to the city center and therefore have a strong impact on the air quality in these cities. Through MAREMIS the degree of impact by ships on air quality will be fully investigated and once this has been determined, recommendations for action will be made to improve air quality.

The consortium is developing Big Data and machine learning-based models and a demonstrator to automatically measure, track, and validate emissions-related aspects of maritime transport to reduce emissions from ships and improve local air quality. Validation is required because the basis for the project is data from the Automatic Identification System. These may be partially erroneous or the ship may have disabled its AIS. It is to be investigated here whether, on the basis of the trajectories as a string of position signals in relation to the AIS navigation status, ships can no longer be detected in the AIS or whether they have deactivated their AIS.

The project will develop and deploy an AI based ship emission model based on real ship movements and validate the AI model using sensor-based emission data. The ship emission model will be used to estimate air pollution from maritime traffic in ports without reckoning on installation of physical sensors and its outputs. It will reflect spatio-temporal emission dynamics and will track traffic emissions in real time and against scenarios.

The impact on regional air quality, i.e. the Northern Germany region as well as Southeast Asia, will be analyzed using a chemical transport model, as these areas dominate the air input to urban areas. Models will continue to be developed to enable evaluation and recommendation of emission reduction strategies through scenario-based changes in port operations and maritime traffic management using big data analysis, simulation, and optimization.