ArticleEnvironmental science & technology2026
Sinking Together? Settling Behavior of Microplastics in the Presence of Fine Sediments.
Article in Environmental science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Predicting microplastic (MP) transport in aquatic environments requires understanding how particle properties, hydrodynamics, and aggregation processes control vertical transport, particularly in turbid systems. Here we investigate how particle physical properties and interactions with fine sediments influence the settling velocity of small MPs (<125 μm) under controlled laboratory conditions. We propose a novel methodology using an optical settling column (SCAF) to measure the settling velocity of MPs with contrasting sizes, shapes, and densities, both as dispersed particles and in mixtures with fine cohesive sediments at concentrations typical of turbid environments, under three treatments: dispersed, preagitated, and prerested. Settling velocities of dispersed MPs were primarily governed by size and density with minor shape effects. Empirical models developed for natural particles accurately predicted the MP settling in this size range. In MP-sediment mixtures, the settling behavior varied markedly with particle properties and treatment conditions. Irregular particles, particularly fragments, exhibited flocculation under all treatments and higher settling velocities, whereas larger spheres interacted weakly with sediments and settled independently. Under prerested conditions, most particles showed enhanced and more uniform settling, with floc velocities converging around ∼0.5-0.6 mm/s. These findings offer insights into aggregation-driven vertical transport and provide a basis for improving predictive models in turbid environments.
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