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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. Environmental Sources Marine & Wildlife Sign in to save

Colour and size influences plastic microbead underestimation, regardless of sediment grain size

The Science of The Total Environment 2018 45 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.
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Summary

White and light-colored microplastic beads were significantly undercounted compared to darker ones in visual assessments, regardless of sediment grain size. This color bias in manual sorting means that common lightweight, light-colored plastic particles may be systematically underreported in environmental microplastic surveys.

Polymers
Study Type Environmental

The quantification of microplastics in environmental samples often requires an observer to determine whether a particle is plastic or non-plastic, prior to further verification procedures. This implies that inconspicuous microplastics with a low natural detection may be underestimated. The present study aimed at assessing this underestimation, looking at how colour (white, green and blue), size (large; ~1000 μm and small; <400 μm) and grain size fraction may affect detection. Sediment treatments varying in grain size were inoculated with known quantities of low-density polyethylene microbeads extracted from commercially bought facial scrubs. These microbeads varied in colour and size. Once extracted using a density separation method microbeads were counted. An overall underestimation of 78.59% may be a result of observer error and/or technical error. More specifically, the results suggested that microbeads varying in colour and size have a different detection probability and that these microbead features are more important in underestimation likelihoods than grain sizes.

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