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Updated: Jul 12, 2026

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
Published on: December 16, 2016
Enhancing microplastic monitoring with ships of opportunity to determine baselines: Methodological design matters
Amy L Lusher1, Svetlana Pakhomova1, Cecilie Singdahl-Larsen1
1Norwegian Institute for Water Research, Oslo, Norway.
Abstract:
Ships of opportunity provide a platform to assess marine pollution without dedicated field campaigns, offering a cost-effective means to expand monitoring coverage. In this study, a microplastic sampling module was integrated into an existing FerryBox system on a commercial ferry (M/S Color Fantasy) operating between Oslo (Norway) and Kiel (Germany). This setup enabled routine acquisition of samples in the Skagerrak and Kattegat areas. Between 2019 and 2022, 39 subsurface samples were collected using an underway pump-based FerryBox module and a filter cascade targeting particles larger than 100 μm, with an average of 7500 L of water filtered. Observed levels of microplastics ranged from not detected to 3.38 items/m3 (average 1.24 items/m3). No correlations were observed between seasonality, environmental parameters and microplastic concentration, indicating that while baseline levels were established, hotspots or environmental drivers could not be identified within this dataset. The study shows how methodological choices - such as spatial coverage, sampled volume, filter mesh size, and laboratory processing protocols - influenced reported concentrations and data comparability. Despite these limitations, resulting concentrations were within the same order of magnitude as other subsurface pump-based investigations, supporting the reliability of the approach. The FerryBox proved versatile and affordable, demonstrating its potential for integration into monitoring programmes. To maximise value of such databases, future efforts should prioritise methodological harmonisation and validation, as inconsistent sampling and analytical protocols currently limit cross-study comparability and hinder detection of broader regional trends. Addressing these issues will ensure datasets are fit-for-purpose and support robust regional and international comparisons.
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