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What can we learn from studying plastic debris in the Sea Scheldt estuary?
Velimirovic, M.; Teunkens, B.; Ghorbanfekr, H.; Buelens, B.; Hermans, T.; Van Damme, S.; Tirez, K.; Vanhaecke, F. (2022). What can we learn from studying plastic debris in the Sea Scheldt estuary? Sci. Total Environ. 851(Part 1): 158226.
In: Science of the Total Environment. Elsevier: Amsterdam. ISSN 0048-9697; e-ISSN 1879-1026, more
Peer reviewed article  

Available in  Authors 

Author keywords
    Plastic pollution; Mesoplastics; Macroplastics; Polymer type; Sea Scheldt estuary; Multi-element fingerprint

Authors  Top 
  • Velimirovic, M., more
  • Teunkens, B., more
  • Ghorbanfekr, H.
  • Buelens, B.
  • Hermans, T.
  • Van Damme, S., more
  • Tirez, K., more
  • Vanhaecke, F., more

    The Sea Scheldt estuary has been suggested to be a significant pathway for transfer of plastic debris to the North Sea. We have studied 12,801 plastic items that were collected in the Sea Scheldt estuary (Belgium) during 3 sampling campaigns (in spring, summer, and autumn) using a technique called anchor netting. The investigation results indicated that the abundance of plastic debris in the Scheldt River was on average 1.6 × 10−3 items per m3 with an average weight of 0.38 × 10−3 g per m3. Foils were the most abundant form, accounting for >88 % of the samples, followed by fragments for 11 % of the samples and filaments, making up for <1 % of the plastic debris. FTIR spectroscopy of 7 % of the total number of plastic debris items collected in the Sea Scheldt estuary (n = 883) revealed that polypropylene (PP), polyethylene (PE), and polystyrene (PS) originating from disposable packaging materials were the most abundant types of polymers. A limited number of plastic debris items (n = 100) were selected for non-destructive screening of their mineral element composition using micro-X-ray fluorescence spectrometry (μXRF). The corresponding results revealed that S, Ca, Si, P, Al, and Fe were the predominant mineral elements. These elements originate from flame retardants, mineral fillers, and commonly used catalysts for plastic production. Finally, machine learning algorithms were deployed to test a new concept for forensic identification of the different plastic entities based on the most important elements present using a limited subset of PP (n = 36) and PE (n = 35) plastic entities.

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