Using optimized particle imaging of micro-Raman to characterize microplastics in water samples

Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection

Microplastics (MPs) and nanoplastics (NPs) present formidable global environmental challenges with serious risks to human health and ecosystem sustainability. Despite their significance, the accurate assessment of environmental MP and NP pollution remains hindered by limitations in existing detection technologies, such as low resolution, substantial data volumes, and prolonged imaging times. Machine learning (ML) provides a promising pathway to overcome these challenges by enabling efficient data processing and complex pattern recognition. This systematic Review aims to address these gaps by examining the role of ML techniques combined with spectroscopy in improving the detection and characterization of NPs. We focused on the application of ML and key tools in MP and NP detection, categorizing the literature into key aspects: (1) Developing tailored strategies for constructing ML models to optimize plastic detection while expanding monitoring capabilities. Emphasis is placed on harnessing the unique molecular fingerprinting capabilities offered by spectroscopy, including both infrared (IR) and Raman spectra. (2) Providing an in-depth analysis of the challenges and issues encountered by current ML approaches for NP detection. This Review highlights the critical role of ML in advancing environmental monitoring and improving our further, deeper investigation of the widespread presence of NPs. By identifying current key challenges, this Review provides valuable insights for future direction in environmental management and public health protection.

Time dependent release of microplastics from disposable face masks poses cyto-genotoxic risks in Allium cepa

The use of disposable face masks (DFMs) increased during the COVID-19 pandemic and has become a threat to the environment due to the release of microplastics (MPs). Although many reports have characterized and explored the release of MPs from DFMs and their effects in aquatic ecosystems, there is a lack of investigation into the effects in terrestrial plants. This report aims to fill this research gap by characterizing whole mask leachates (WMLs) collected at different time points and examining their toxicity on Allium cepa, a terrestrial model plant. Various analytical techniques including FE-SEM, FT-IR, and Raman spectroscopy were used to identify MPs in WMLs. The MPs are composed of polypropylene mostly and the concentration of smaller-sized MPs increased with leachate release time. The WMLs showed a MP concentration-dependent cytogenotoxic effect (72 %, 50 %, and 31 %, on 1, 5, and 11-day WMLs, respectively) on A. cepa root cells due to elevated oxidative stress (19 %, 45 %, and 70 %, on 1, 5, and 11-day WMLs, respectively). Heavy metal content of the WMLs was negligible and, thus, not a significant contributor to toxicity in the plant. Overall, this report highlights the fate of DFMs in the environment and their biological impacts in a model plant.

The release of polypropylene plastic from disposable face masks in different water conditions and their potential toxicity in human cells

Due to their extensive use during and after the COVID-19 pandemic, many disposable face masks are irresponsibly deposited into the water environment, threatening the health of people living nearby. However, the effects of water conditions on the degradation and potential hazards of these masks are generally unclear. This paper entailed the release and cellular toxicity of micro/nano plastics from disposable face masks once discarded in different waters, including soil water, river water, and tap water, with deionized (DI) water as control. At first, polypropylene (PP) was confirmed to be the major component of disposable face masks with Raman and Fourier transform infrared (FTIR) techniques. To monitor the release rate of PP from masks, a silver nanoparticle (AgNP)-based surface-enhanced Raman scattering (SERS) method was established by employing the unique Raman fingerprint of PP at 2882 cm-1. During 30-d incubation in different waters, the release rates of PP, sizes of PP aggregates, length of fibers, and proportions of plastics smaller than 100 nm were in the order of soil water > river water > tap water > DI water. All the obtained PP exhibited significant toxicity in human lung cancer (A549) cells at concentrations of 70 mg/L for 48 h, and the ones obtained in soil water exhibited the most severe damage. Overall, this paper revealed that environmental waters themselves would worsen the adverse effects of disposable face masks, and the key compounds affecting the degradation of masks remain to be clarified. Such information, along with the established methods, could be beneficial in assessing the health risks of disposable face masks in different waters.