Probing nanoplastics derived from polypropylene face masks with hyperspectral dark-field microscopy.

The high worldwide consumption of cheap plastic goods has already resulted in a serious environmental plastic pollution, exacerbated by piling of disposed personal protective equipment because of the recent outbreak of COVID-19. The aim of this study was to assess the feasibility of dark-field hyperspectral microscopy in the 400-1000 wavelength range for detection of nanoplastics derived from weathered polypropylene masks. A surgical mask was separated to layers and exposed to UV radiation (254 nm) for 192 h. Oxidative degradation of the polypropylene was evidenced by ATR FT-IR analysis. UV treatment for 192 h resulted in generation of differently shaped micro- and nano-sized particles, visualized by dark-field microscopy. The presence of nanoparticles was confirmed by AFM studies. The hyperspectral profiles (400-1000 nm) were collected after every 48 h of the UV treatment. The distinct hyperspectral features faded after prolonged UV exposure, but the assignment of some particles to either blue or white layers of mask could still be made based on spectral characteristics.

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples. This study evaluated the application of a combined technique based on dark-field enhanced microscopy and hyperspectral imaging augmented with deep learning data analysis for the visualisation, detection and identification of microplastic particles released from commercially available medical masks after 192 hours of UV-C irradiation. The analysis was performed using a separated blue-coloured spunbond outer layer and white-coloured meltblown interlayer that allowed us to assess the influence of the structure and pigmentation of intact and UV-exposed samples on classification performance. Microscopy revealed strong fragmentation of both layers and the formation of microparticles and fibres of various shapes after UV exposure. Based on the spectral signatures of both layers, it was possible to identify intact materials using a convolutional neural network successfully. However, the further classification of UV-exposed samples demonstrated that the spectral characteristics of samples in the visible to near-infrared range are disrupted, causing a decreased performance of the CNN. Despite this, the application of a deep learning algorithm in hyperspectral analysis outperformed the conventional spectral angle mapper technique in classifying both intact and UV-exposed samples, confirming the potential of the proposed approach in secondary microplastic analysis.