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Hyperspectral reflectance dataset of pristine, weathered, and biofouled plastics

Journal of Biochemical and Molecular Toxicology 2023 8 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Giulia Leone, Ana I. Catarino, Liesbeth De Keukelaere, Mattias Bossaer, Els Knaeps, Gert Everaert

Summary

This work presents a hyperspectral reflectance dataset of pristine, weathered, and biofouled plastic samples collected from Belgian waterways, measured under dry, wet, and submerged conditions. The dataset follows FAIR data principles and is intended to support development of remote sensing approaches for detecting macroplastic pollution in aquatic environments.

Study Type Environmental

Abstract. This work presents a hyperspectral reflectance dataset of macroplastic samples acquired using Analytical Spectral Devices (ASD) FieldSpec 4. Samples analyzed consisted of pristine, artificially weathered, and biofouled plastic items and plastic debris samples collected in the docks of the Port of Antwerp and in the river Scheldt near Temse Bridge (Belgium). The hyperspectral signal of each sample was measured in controlled dry conditions in an optical calibration facility at the Vlaamse Instelling voor Technologisch Onderzoek (VITO; Flemish Institute for Technological Research) and, for a subset of plastics, under wet and submerged conditions in a silo tank at Flanders Hydraulics. The wet and submerged hyperspectral signals were measured in a mesocosm setting that mimicked environmentally relevant concentrations of freshwater microalgae and suspended sediment. The ASD was equipped with an 8∘ field of view at the calibration facility, and a 1∘ field of view was used in the mesocosm setting. The dataset obtained complies with the FAIR (Findable, Accessible, Interoperable, Reusable) principles and is available in the open-access repository Marine Data Archive (https://doi.org/10.14284/530, Leone et al., 2021).

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