Abstract
Even though offshore energy production has risen sharply in the last decades, not all possible effects of offshore wind farms (OWFs) are monitored yet and potential risks resulting from chemical emissions of these structures are still largely unknown. Within the EU Interreg project Anemoi, chemical emissions from OWFs are investigated. Sources of chemical emissions are, amongst others, the particulate release of corrosion protection coatings and paints, as well as leading edge erosion of rotor blades. In both cases, particles are directly introduced into the marine environment. Even though paint particles are introduced into the marine environment not only by OWFs, but a wide range of sources, at the current time only a small number of studies have investigated these particles in marine samples. This is related to inconsistencies regarding their classification as microplastics, and to methodical challenges which hamper the analysis of these particles in environmental samples.
Before beginning to analyse coating particles in sediment collected in and near OWFs, we first tested multiple methodical approaches with respect to their suitability to separate these particles from the sample matrix. Spiked samples were prepared comprising marine sediment (sandy or silty) and coating particles. Six coatings commonly used on offshore wind turbines (four epoxy-based and two polyurethane-based) from two manufacturers were tested. The sample processing included density separation and digestion steps as well as the identification of particles performed visually, by FTIR, and Pyrolysis GC/MS. Two different salt solutions, differing in their density, were applied for density separation of coating and sediment particles and recovery rates were calculated. In parallel, the chemical resistance of coating particles towards various digestion protocols was tested. It became clear that the diverging physical (e.g., density, brittleness) and chemical properties of these type of particles compared to common plastics require attention at all stages of sample processing.
