The MAREMIS project, funded by the German Federal Ministry of Education and Research (BMBF), aimed to reduce maritime emissions and improve coastal air quality by developing a data‑driven ship‑emission model, coupling it to a regional climate‑chemistry framework, and delivering a demonstrator for port operators. The effort was carried out jointly by German and Singaporean partners, focusing on the ports of Hamburg and Singapore. Over the course of the project, the consortium assembled high‑resolution emission inventories, performed detailed atmospheric simulations, and created an interactive dashboard to support decision‑making.

The technical work unfolded in three main stages. First, a ship‑emission model was constructed that draws on real vessel trajectories and sensor‑based emission data. For Hamburg, the model incorporated the German Environment Agency’s (UBA) GRETA inventory, which offers a 1 km spatial resolution, while for Singapore it used ship‑emission estimates from the DLR‑KN calculations. The model was fed with CAMS‑GLOB (v4.2) and CAMS‑SHIP (v2.1) emissions, processed through the AP310 system, and combined with the GRETA data to produce a comprehensive maritime emission field. Second, the model outputs were fed into the MECO(n) climate‑chemistry system, which integrates the global EMAC model (T106L90MA, 180 km horizontal resolution, 90 vertical levels) with nested regional COSMO/MESSy domains. For North Germany, the nesting hierarchy reached a 2 km resolution (0.01°) over the target area, while for Singapore the finest domain was 12 km (0.1°). The atmospheric component was driven by ERA5 reanalysis data, relaxed in the AP320 system, and run in the quasi‑chemistry transport model (QCTM) mode to isolate the impact of different emission scenarios on regional air quality. Third, the results were integrated into a demonstrator system that visualises the spatial and temporal evolution of pollutants, including CO₂, NOₓ, and ground‑level ozone, and quantifies the relative contribution of ship emissions to these species.

Key scientific outcomes include the quantification of CO₂ emissions from maritime traffic in both ports and the demonstration that targeted operational changes can yield measurable reductions. The model revealed that the best‑performing scenario for Hamburg reduced CO₂ emissions by a substantial margin compared with the reference case, while a similar trend was observed for Singapore. NOₓ mixing ratios were shown to decrease in the vicinity of the ports when electric vessels or shore power were introduced, with reductions in the range of several percent relative to the baseline. Ground‑level ozone concentrations were also affected; the relative contribution of ship emissions to ozone was quantified, and the demonstrator highlighted how mitigation strategies could lower ozone levels in adjacent urban areas. The visualisation platform, presented in a dashboard view, allows port authorities and policymakers to explore these scenarios interactively, supporting evidence‑based decisions on traffic management and infrastructure upgrades.

Collaboration across the German and Singaporean consortia was essential for data sharing and model validation. German partners supplied the GRETA inventory and coordinated the EMAC and COSMO/MESSy simulations, while Singaporean collaborators provided the ship‑emission estimates and facilitated the application of the model to the Singapore port. The project’s timeline included a focused data‑collection period in 2019, during which two weeks of stable meteorological conditions were selected for detailed analysis. Throughout the project, the BMBF provided financial support, and the consortium maintained close communication to ensure that the modelling framework met the needs of both scientific and operational stakeholders. The final report documents the methodology, results, and the demonstrator’s potential to guide future maritime emission reduction strategies.