August 11 2025

Hard Data and Virtual Plankton — How We Model Ship Traffic’s Impact on the Arctic

Within ICEBERG, our goal is to resolve some of the potential impacts of ship traffic on the marine plankton on The North Atlantic Arctic region. In order to do this, we combine model simulations with Arctic Ship Traffic Data (ASTD). Read further to know why and how this modelling is done.

The North Atlantic Arctic region is characterized by significant connectivity between its oceanic subregions, driven by both human activity and natural oceanographic processes.

On the one hand, growing ship traffic, particularly along emerging and established Arctic shipping routes connects ports and coastal communities. However, the ship traffic along these connections is also a source of pollutants. For example, by discharging wastewater vessels add nutrients, pharmaceuticals, as well as micro- and nanoplastics to the environment.

On the other hand, powerful ocean currents, like the North Atlantic Drift and the East Greenland Current, create physical and ecological linkages by transporting water masses, heat, nutrients, and marine organisms across vast distances. These same currents also facilitate the long-range transport of pollutants, including contaminants originating far outside the Arctic.

Together, these interconnected pathways shape the region’s environmental health, economic potential, and geopolitical significance.

The North Atlantic Arctic region is characterized by significant connectivity between its oceanic subregions.

Ship traffic depends on the season

Shipping activity in the North Atlantic Arctic region follows distinct seasonal patterns, largely dictated by sea ice extent and weather conditions.

Most traffic is concentrated in the ice-free summer and early autumn months, when navigation routes become better accessible. Recent and future changes in sea ice cover may impact these seasonal patterns. Furthermore, tourism-related vessel traffic has become more prominent in recent years, even though the overall number of tourist ships remains relatively low compared to cargo or fishing vessels.

Figures from the ICEBERG research area, Svalbard, Iceland and Greenland. The figures show trends in vessel counts, composition of ship types, greywater by ship type, blackwater by ship type and garbage by ship type.
Figure 1: Marine traffic trends and patterns in the ICEBERG region. The number of individual ships by month is compared across three years in (a) for the study region outlined in (c). The composition of vessel types is broken down for 2022 in (b). Associated vessel-type-specific emissions are estimated for greywater (d), blackwater (e), and garbage (f). The colour legend given in (g) applies to (b) and (d)-(f). Data source: PAME/ASTD.

However, the cruise ships tend to have a disproportionately large environmental footprint. These ships generate significantly higher volumes of waste per voyage, including graywater (from sinks, showers, and laundries), as well as black water (from toilets), which are treated differently amongst ships before discharge. This seasonal influx of wastewater adds a layer of environmental pressure to an already sensitive and rapidly changing ecosystem.

How we model the impact on plankton ecosystem

Within ICEBERG, our goal is to resolve some of the potential impacts of these discharges on the marine plankton. In order to do this, we combine model simulations with Arctic Ship Traffic Data (ASTD), provided by the working group Protection of the Arctic Marine Environment (PAME) of the Arctic Council.

These data resolve ship tracks from AIS (automatic identification system) positions and estimated volumes of wastewater discharged by the ships.

Two maps of the Arctic area, focused on the ICEBERG case study sites, Iceland, Svalbard and Greenland. The first map shows the movement of particles that were released in the Ocean as a part of the research. On the other map, there are colours around the research areas, indicating the volume of ship traffic in these areas.
Figure 2: Connectivity between ICEBERG case study sites.
(a) Particle tracking analyses reveal potential pathways of surface water parcels. Particles were released during summer time at the three case study sites (Svalbard = red, Iceland = turquoise, Greenland = gold) and tracked forward (darker colors) and backward (lighter colors) in time using velocity fields from a numerical ocean model (FOCI, 0.5×0.5°, SSP370).
(b) Marine traffic density in 2022 highlights areas of high vessel activity, particularly along shorelines and major shipping routes connecting regional ports (data source: PAME/ASTD).

The impact on the plankton ecosystem will be simulated and assessed along water masses that are passing through the three ICEBERG case study sites. For this, we use ensembles of model trajectories (pathways of ‘virtual plankton’ drifting with the ocean currents) that cross the respective case study sites during the months between April through October. Along their journey, the growth of the plankton will be affected differently by the ships’ discharges.

Our modelling approach allows us to compare a plankton ecosystem affected by wastewater discharges from marine traffic with a ‘control ecosystem’ that is not perturbed by marine traffic.

Such comparisons enable us to quantify the impact of wastewater pollution associated with ships. It also helps us identify times and locations when and where the discharge—e.g. from cruise ships— has minimal impact or where it should be better avoided.

Written by Markus Schartau and Vanessa Lampe (GEOMAR).

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Project Scientific Coordinator

Prof. Thora Herrmann
University of Oulu
thora.herrmann@oulu.fi

Co-coordinator, Project Manager

Dr Élise Lépy
University of Oulu
elise.lepy@oulu.fi

Communications

Marika Ahonen
Kaskas
marika.ahonen@kaskas.fi

Innovative Community Engagement for Building Effective Resilience and Arctic Ocean Pollution-control Governance in the Context of Climate Change

ICEBERG has received funding from the European Union's Horizon Europe Research and innovation funding programme under grant agreement No 101135130

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