Microplastics (<5 mm) have been documented in environmental samples on a global scale. While these pollutants may enter aquatic environments via wastewater treatment facilities, the abundance of microplastics in these matrices has not been investigated. Although efficient methods for the analysis of microplastics in sediment samples and marine organisms have been published, no methods have been developed for detecting these pollutants within organic-rich wastewater samples. In addition, there is no standardized method for analyzing microplastics isolated from environmental samples. In many cases, part of the identification protocol relies on visual selection before analysis, which is open to bias. In order to address this, a new method for the analysis of microplastics in wastewater was developed. A pretreatment step using 30% hydrogen peroxide (H2O2) was employed to remove biogenic material, and focal plane array (FPA)-based reflectance micro-Fourier-transform (FT-IR) imaging was shown to successfully image and identify different microplastic types (polyethylene, polypropylene, nylon-6, polyvinyl chloride, polystyrene). Microplastic-spiked wastewater samples were used to validate the methodology, resulting in a robust protocol which was nonselective and reproducible (the overall success identification rate was 98.33%). The use of FPA-based micro-FT-IR spectroscopy also provides a considerable reduction in analysis time compared with previous methods, since samples that could take several days to be mapped using a single-element detector can now be imaged in less than 9 h (circular filter with a diameter of 47 mm). This method for identifying and quantifying microplastics in wastewater is likely to provide an essential tool for further research into the pathways by which microplastics enter the environment.

Fenton's reagent was used to isolate microplastics from organic-rich wastewater. The catalytic reaction did not affect microplastic chemistry or size, enabling its use as a pre-treatment method for focal plane array-based micro-FT-IR imaging. Compared with previously described microplastic treatment methods, Fenton's reagent offers a considerable reduction in sample preparation times.

Microplastics (<5 mm particles) occur within both engineered and natural freshwater ecosystems, including wastewater treatment plants, lakes, rivers, and estuaries. While a significant proportion of microplastic pollution is likely sequestered within freshwater environments, these habitats also constitute an important conduit of microscopic polymer particles to oceans worldwide. The quantity of aquatic microplastic waste is predicted to dramatically increase over the next decade, but the fate and biological implications of this pollution are still poorly understood. A growing body of research has aimed to characterize the formation, composition, and spatiotemporal distribution of microplastic-associated ("plastisphere") microbial biofilms. Plastisphere microorganisms have been suggested to play significant roles in pathogen transfer, modulation of particle buoyancy, and biodegradation of plastic polymers and co-contaminants, yet investigation of these topics within freshwater environments is at a very early stage. Here, what is known about marine plastisphere assemblages is systematically compared with up-to-date findings from freshwater habitats. Through analysis of key differences and likely commonalities between environments, we discuss how

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Determining microplastics in environmental samples quickly and reliably is a challenging task. With a largely automated combination of optical particle analysis, Fourier transform infrared (FT-IR), and Raman microscopy along with spectral database search, particle sizes, particle size distributions, and the type of polymer including particle color can be determined. We present a self-developed, open-source software package for realizing a particle analysis approach with both Raman and FT-IR microspectroscopy. Our software GEPARD (Gepard Enabled PARticle Detection) allows for acquiring an optical image, then detects particles and uses this information to steer the spectroscopic measurement. This ultimately results in a multitude of possibilities for efficiently reviewing, correcting, and reporting all obtained results.