Imagine the Arctic as a giant filter that is now trapping plastics and chemicals as the ice melts. This work tracks how these pollutants move through the water and end up in the food we eat. It uses drones and AI to spot trash on beaches and helps local people create plans to keep their environment clean.
Arctic shipping and tourism are increasing, but there is a lack of real-time data on how plastics and chemicals contaminate the food chain and coastal environments. This creates regulatory risks and health hazards for local communities.
AI-driven marine litter detection tools, drone-based survey systems, and Lagrangian trajectory models for pollutant dispersion.
If you are a monitoring firm dealing with the difficulty of surveying remote coastlines — this project developed AI-driven marine litter detection and drone-based surveys that automate the identification of pollution.
If you are a shipping operator dealing with strict emissions regulations — this project developed Lagrangian trajectory modeling and ship emissions data tools to simulate how pollutants disperse in the ocean.
If you are a producer dealing with contaminant risks in the food chain — this project developed an exposomics approach to characterize chemical contaminants and their impact on human digestive health.
Based on available project data, there is no specific pricing or cost mentioned for the tools developed; the project received an EU contribution of EUR 5,987,060 for research and development.
The project uses drones and AI for marine litter detection in three regional case studies. Based on available project data, these are currently being tested in specific Arctic gateway regions rather than a global industrial scale.
Based on available project data, specific IP or licensing terms are not listed. The project focuses on co-creation and community-led governance.
The project aims to influence policy at EU and Arctic levels by generating knowledge on ship emissions and pollutant dispersion through modeling and in-situ observations.
The project period runs from 2024-01-01 to 2027-06-30, with key activities already performed in the first 18 months.
The consortium is heavily weighted toward research and academia, with 13 university and research entities out of 16 partners. Industrial participation is low at 12% (2 SMEs), suggesting the project is currently in a technology-validation phase rather than a commercial-deployment phase. The geographical spread across 9 countries ensures a wide range of regulatory and environmental data points.
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