Representative subsampling methods for the chemical identification of microplastic particles in environmental samples

Abstract

Chemical identification of microplastics is time-consuming, especially when particles are numerous. To save resources, a subsample of particles is often selected for chemical identification. Because no standard subsampling protocols currently exist, methods vary widely and often lack evidence of representativeness, limiting conclusions and cross-study comparability. In this study, we determine best practices for subsampling >100 μm microparticles for chemical identification based on two research objectives: 1) quantifying the proportion of plastic, anthropogenic and natural particles and 2) quantifying the diversity of material types. Using published datasets where all microparticles counted were chemically identified, we tested subsampling methods where particles are selected either from individual samples, or from a group of samples treated collectively. We determine that overall, particle selection at random provides a representative subsample with the lowest effort. Subsampling methods must also be informed by your research objective. Fewer particles are required to accurately represent the proportion of plastic, anthropogenic and natural particles present, compared to representing the diversity of material types. To accurately represent particle diversity, researchers must understand particle diversity within the environmental matrix in question which informs necessary sampling volume. Overall, harmonized, and representative subsampling practices will allow improved comparability among studies, transparent data reporting, and more robust conclusions.