The analysis of environmental occurrence of microplastic (MP) particles has gained notable attention within the past decade. An effective risk assessment of MP litter requires elucidating sources of MP particles, their pathways of distribution and, ultimately, sinks. Therefore, sampling has to be done in high frequency, both spatially and temporally, resulting in a high number of samples to analyze. Microspectroscopy techniques, such as FTIR imaging or Raman particle measurements allow an accurate analysis of MP particles regarding their chemical classification and size. However, these methods are time-consuming, which gives motivation to establish subsampling protocols that require measuring less particles, while still obtaining reliable results. The challenge regarding the subsampling of environmental MP samples lies in the heterogeneity of MP types and the relatively low numbers of target particles. Herein, we present a comprehensive assessment of different proposed subsampling methods on a selection of real-world samples from different environmental compartments. The methods are analyzed and compared with respect to resulting MP count errors, which eventually allows giving recommendations for staying within acceptable error margins. Our results are based on measurements with Raman microspectroscopy, but are applicable to any other analysis technique. We show that the subsampling-errors are mainly due to statistical counting errors (i.e., extrapolation from low numbers) and only in edge cases additionally impacted by inhomogeneous distribution of particles on the filters. Keeping the subsampling-errors low can mainly be realized by increasing the fraction of MP particles in the samples.

The Official Journal of EuroPCR and the European Association of Percutaneous Cardiovascular Interventions (EAPCI)

During recent years plastics became one of the focuses of EU policy. A harmonisation and comparability of microplastics monitoring results across Europe is needed. The complexity of microplastic data makes it necessary to develop a specific, tailor-made database rather than adapting and modifying one of the existing databases. To meet this demand, we present a publicly accessible, flexible, and extendable structured relational database for particle-based microplastic data. The developed relational database is adaptive and meets the specific demands of microplastics, e.g., a large variety of sampling, processing and analytical methods, many types of plastics, and a very wide size spectrum ranging from micrometres to millimetres. In this paper we discuss the development of the database, data entry specifics, sample analysis methods, microplastics data manipulation and quality assurance, and database integration and accessibility.