A novel online method for the detection, analysis, and classification of airborne microplastics

The continued increase in global plastic production and poor waste management ensures that plastic pollution is a serious environmental concern for years to come. Because of their size, shape, and relatively low density, primary or secondary plastic particles in the environment between 1-1000 µm in size (known as microplastics, or MPs) may be entrained (and/or re-entrained) into the atmosphere through processes similar to other coarse-mode particles, such as mineral dust. MPs can thus be advected over great distances, reaching even the most pristine and remote areas of the Earth, and may have significant negative consequences for humans and the environment. The detection and analysis of MPs once airborne, however, remains a challenge because most observational methods are offline and resource-intensive, and, therefore, are not capable of providing continuous quantitative information.

In this study, we present results using an online, in situ airflow cytometer (SwisensPoleno Jupiter; Swisens AG; Horw, Switzerland) – coupled with machine learning – to detect, analyze, and classify airborne, single-particle MPs in near real time. The performance of the instrument to differentiate single-particle MPs of five common polymer types was investigated under laboratory conditions using combined information about their size and shape (determined using holographic imaging) and intrinsic fluorescence, known as autofluorescence, measured using three excitation wavelengths and five emission detection windows. The classification capability using these methods was determined alongside other coarse-mode aerosol with similar morphology or autofluorescence characteristics, such as a mineral dust and several pollen taxa.

The tested MPs exhibit a measurable autofluorescence signal that not only allows them to be distinguished from the other particles in this study demonstrating autofluorescence properties, such as pollen, but can also be differentiated from each other, with high (>90%) classification accuracy based on their multispectral autofluorescence signatures and morphology. The results using the presented novel methods are expected to provide a foundation towards significantly improving the understanding of properties and types of MPs present in the atmosphere.