0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Sign in to save

What can we learn from studying plastic debris in the Sea Scheldt estuary?

The Science of The Total Environment 2022 19 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Milica Velimirović Milica Velimirović Milica Velimirović Milica Velimirović Milica Velimirović Bert Teunkens, Kristof Tirez, Kristof Tirez, Bert Teunkens, Milica Velimirović Frank Vanhaecke, Milica Velimirović H. Ghorbanfekr-Kalashami, Milica Velimirović Milica Velimirović Kristof Tirez, Kristof Tirez, Bart Buelens, Frank Vanhaecke, Milica Velimirović Milica Velimirović Milica Velimirović Frank Vanhaecke, Milica Velimirović Milica Velimirović Kristof Tirez, Tom J.N. Hermans, Tom J.N. Hermans, S. Van Damme, Kristof Tirez, Frank Vanhaecke, Milica Velimirović Kristof Tirez, Kristof Tirez, S. Van Damme, Frank Vanhaecke, Milica Velimirović Kristof Tirez, Milica Velimirović Milica Velimirović Milica Velimirović Milica Velimirović Kristof Tirez, Frank Vanhaecke, Frank Vanhaecke, Frank Vanhaecke, Milica Velimirović Milica Velimirović Frank Vanhaecke, Milica Velimirović

Summary

Researchers analyzed 12,801 plastic items collected from the Sea Scheldt estuary in Belgium across three seasons, finding that polypropylene and polyethylene packaging films dominated (over 88% of items by count), and demonstrating that machine learning applied to elemental composition data can forensically distinguish plastic types.

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 items per m with an average weight of 0.38 × 10 g per m. 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.

Sign in to start a discussion.

Share this paper