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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. Detection Methods Environmental Sources Marine & Wildlife Sign in to save

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2022 Score: 25 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Giulia Leone Giulia Leone Giulia Leone Giulia Leone Giulia Leone

Summary

This reply to a peer reviewer describes a hyperspectral reflectance dataset collected from plastic samples under different conditions — pristine, weathered, biofouled, wet, and submerged — to support remote sensing detection of plastic litter in and around water. Developing reliable spectral signatures for plastics under real-world conditions is essential for remote monitoring of plastic pollution.

Study Type Environmental

This work presents a hyperspectral reflectance dataset of macroplastic samples acquired using Analytical Spectral Devices (ASD) FieldSpec 4. Samples analysed 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 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 of 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 Findability, Accessibility, Interoperability, and Reuse (FAIR) 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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