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Engineered Polystyrene-Based Microplastics of High Environmental Relevance.

Amit Kumar SarkarAndrey Ethan RubinInes Zucker
Published in: Environmental science & technology (2021)
Microplastic (MP) pollution-an emerging environmental challenge of the 21st century-refers to accumulation of environmentally weathered polymer-based particles with potential environmental and health risks. Because of technical and practical challenges when using environmental MPs for risk assessment, most available data are generated using plastic models of limited environmental relevancy (i.e., with physicochemical characteristics inherently different from those of environmental MPs). In this study, we assess the effect of dominant weathering conditions-including thermal, photo-, and mechanical degradation-on surface and bulk characteristics of polystyrene (PS)-based single-use products. Further, we augment the environmental relevance of model-enabled risk assessment through the design of engineered MPs. A set of optimized laboratory-based weathering conditions demonstrated a synergetic effect on the PS-based plastic, which was fragmented into millions of 1-3 μm MP particles in under 16 h. The physicochemical properties of these engineered MPs were compared to those of their environmental counterpart and PS microbeads often used as MP models. The engineered MPs exhibit high environmental relevance with rough and oxidized surfaces and a heterogeneous fragmented morphology. Our results suggest that this top-down synthesis protocol combining major weathering mechanisms can fabricate improved, realistic, and reproducible PS-based plastic models with high levels of control over the particles' properties. Through increased environmental relevancy, our plastic model bolsters the field of risk assessment, enabling more reliable estimations of risk associated with an emerging pollutant of global concern.
Keyphrases
  • human health
  • risk assessment
  • life cycle
  • heavy metals
  • randomized controlled trial
  • climate change
  • escherichia coli
  • machine learning
  • pseudomonas aeruginosa
  • air pollution
  • candida albicans
  • health risk assessment