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Surface properties and rising velocities of pristine and weathered plastic pellets.

Tom BondJack MortonZeinab Al-RekabiDavid CantStuart DavidsonYiwen Pei
Published in: Environmental science. Processes & impacts (2022)
This study compared the surface properties and rising velocities of pristine and weathered plastic production pellets, to evaluate impacts of environmental conditions. Rising velocities were measured for 140 weathered pellets collected from a Spanish beach and compared with pristine low-density polyethylene, high-density polyethylene and polypropylene pellets. A subset of 49 weathered pellets were analysed by Fourier-transform infrared spectroscopy (FTIR), with all found to be polyethylene. Experimental rising velocities for the weathered pellets varied widely, from (2.36 ± 0.01) cm s -1 to (10.56 ± 0.26) cm s -1 , with a mean value of (5.79 ± 0.06) cm s -1 . Theoretical rising velocities were consistently higher than experimental velocities for all pellet types: on average 136% of experimental values for weathered pellets. This discrepancy was more distinct for less spherical pellets, which were often more weathered. Flatter pellets often oscillated as they rose, which explains at least some of this finding. Atomic force microscopy (AFM) analysis revealed that the roughness of the pristine and weathered pellets was (59 ± 11) nm, and (74 ± 26) nm respectively. X-ray photoelectron spectroscopy (XPS) analysis showed that the proportion of surface oxidised carbon species were 2.3% and 4.0% of the total carbon signal for a pristine and a weathered pellet, respectively; consistent with photochemical reactions changing the surface chemistry of weathered pellets. As determined by density column, weathered pellets had slightly lower experimental densities than pristine pellets. Overall, this study illustrates why it is important that modelling studies on the environmental fate and/or movements of microplastics validate or correct predictions using experimental data.
Keyphrases
  • atomic force microscopy
  • photodynamic therapy
  • machine learning
  • risk assessment
  • magnetic resonance
  • computed tomography
  • human health
  • big data
  • deep learning