Analysis techniques for quantifying nano-and microplastic particles and their degradation in the marine environment (ANDROMEDA) (Analysis techniques for quantifying nano-and microplastic particles and their degradation in the marine environment)
Project details
Status: Completed
Project period: 2020–2023
Funding source: Norges forskningsråd (311313)
Coordinating institution: NILU
Current methods for microplastic (MP) analysis can be divided into low-cost versus more advanced methods. ANDROMEDA recognizes that further development and validation is needed for both approaches. Low-cost methods are needed to identify a broad range of MP polymers. Advanced methods need further development to push the limit of detectability for smaller sizes of MP and nanoplastics (NP). Within ANDROMEDA, in situ MP detection, efficient sampling and cost-effective laboratory methods will be optimized to. Approaches will be based on hyperspectral imaging, chemical markers and fluorometric detection techniques to quantify and characterize MP and NP down to 1 µm, 0.2 µm or lower. Specific tasks will focus on challenging types of MP such as microfibers, tire wear particles (TWPs) and paint flakes. Quality assurance will be a central theme in all aspects of the project. Communication and data management will ensure strong stakeholder involvement and outreach of the project results.
Publications
- Uptake of organic contaminants from car tire microplastics in Arctic marine species
- Leaching of chemicals and DOC from tire particles under simulated marine conditions
- Uptake of chemicals from tire wear particles into aquatic organisms – search for biomarkers of exposure in blue mussels
- Ingestion of car tire crumb rubber and uptake of associated chemicals by lumpfish (Cyclopterus lumpus)
- Car tire particles and their additives: biomarkers for recent exposure in marine environments
- Aging of Tire Particles in Deep-Sea Conditions: Interactions between Hydrostatic Pressure, Prokaryotic Growth and Chemical Leaching
- From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red‑based marine microplastic identification
- Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining
- UV-degradation is a key driver of the fate and impacts of marine plastics. How can laboratory experiments be designed to effectively inform risk assessment?