Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics

Abstract

Abstract. The continued increase in global plastic production and poor waste management ensures that plastic pollution will be a serious environmental concern for years to come. Because of their size, shape, and relatively low density, plastic particles between 1 and 1000 µm in size (known as microplastics or MPs) emitted directly into the environment (“primary”) or created due to degradation (“secondary”) may be transported through the atmosphere, similarly 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 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, not capable of providing continuous quantitative information. In this study, we present results using an online in situ airflow cytometer (SwisensPoleno Jupiter; Swisens AG; Emmen, Switzerland) – coupled with machine learning – to detect, analyze, and classify airborne single-particle MPs in near real time. The performance of the instrument in differentiating between single-particle MPs of five common polymer types (including polypropylene, polyethylene, polyamide, poly(methyl methacrylate), and polyethylene terephthalate) was investigated under laboratory conditions using combined information about their size and shape (determined using holographic imaging) and fluorescence measured using three excitation wavelengths and five emission detection windows. The classification capability using these methods was determined alongside other coarse-mode aerosol particles with similar morphology or fluorescence characteristics, such as a mineral dust and several pollen taxa. The tested MPs exhibit a measurable fluorescence signal that not only allows them to be distinguished from other fluorescent particles, such as pollen, but also differentiated from each other, with high (> 90 %) classification accuracy based on their multispectral fluorescence signatures. The classification accuracies of machine learning models using only holographic images of particles, only the fluorescence response, and combined information from holography and fluorescence to predict particle types are presented and compared. The last model, using both the holographic images and fluorescence information for each particle, was the most optimal model used, providing the highest classification accuracy compared to employing models using only the holography or fluorescence response separately. The results provide a foundation for significantly improving the understanding of the properties and types of MPs present in the atmosphere.