Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics - N. D. Beres; N. D. Beres; J. Burkart; J. Burkart; E. Graf; Y. Zeder; L. A. Dailey; B. Weinzierl (Atmospheric Measurement Techniques)
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.