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Anionic nanoparticle and microplastic non-exponential distributions from source scale with grain size in environmental granular media
journal contribution
posted on 2020-11-17, 00:00 authored by Anna Rasmuson, Brett Peters, Brock Erickson, Cesar Ron, Diogo BolsterDiogo Bolster, Kurt Vaness, William Peil JohnsonNanoparticle and microplastic (colloid) transport behaviors impact strategies for groundwater protection and remediation. Complex colloid transport behaviors of anionic nano- and micro-sized colloids have been previously elucidated via independent experiments in chemically-cleaned and amended granular media with grain sizes in the range of fine to coarse sand (e.g., 200-1000 um). Such experiments show that under conditions where a repulsive barrier was present in colloid-collector interactions (unfavorable conditions), the distribution of retained colloids down-gradient from their source deviates from the exponential decrease expected from compounded loss across a series of collectors (grains). Previous experiments have not examined the impact of colloid size or granular media grain size on colloid distribution down-gradient from their source, particularly in streambed-equilibrated granular media. To address this gap, a field transport experiment in constructed wetland stream beds to distances up to 20 m were conducted for colloids ranging in size from micro to nano (60 nm-7 mu m) in streambed-equilibrated pea gravel and sand (420 mu m and 420 mu m mean grain sizes, respectively). All colloid sizes showed non-exponential (hyper-exponential) distributions from source, over meter scales in pea gravel versus cm scales reported for fine sand. Colloids in the ca. 1 mu m size range were most mobile, as expected from mass transfer to surfaces and interaction with nanoscale heterogeneity. The distance over which non-exponential colloid distribution occurred increased with media grain size, which carries implications for the potential mechanism driving non-exponential colloid distribution from source, and for strategies to predict transport. (C) 2020 Elsevier Ltd. All rights reserved.
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Date Created
2020-09-01Date Modified
2020-11-17Language
- English
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