New Perspective on the Mobilization of Microplastics through Capillary Fringe Fluctuation in a Tidal Aquifer Environment
Bibliographic record
Abstract
The presence of plastic fragments in the environment is a growing global concern. In this study, we explored the effects of dynamic fluctuations of capillary fringe on the transport of microplastics (MPs) in the substrate combining various environmental and MP properties. Both experimental and Hydrus-2D modeling results confirmed that increasing cycles of water table fluctuation led to the rise of capillary fringe. An increase in the cycles of water table fluctuations did not significantly change the overall MP retention percentages in 0.5 mm substrate but altered the MP distribution along the column. In 1 and 2 mm substrate, the increase in cycle numbers enhanced the MP transport from substrate to the water below. In terms of the size of the MPs, more 20-25 μm polyethylene (PE2) were retained in the substrate compared to 4-6 μm polyethylene (PE1) under the same number of fluctuation cycles. High-density polytetrafluoroethylene (PTFE, 5-6 μm) exhibited higher retention percentages compared to PE1 particles. Ultraviolet aging for 60 days enhanced PE1 transport along the column, while 60 days of seawater aging did not affect PE1 transport greatly. For PTFE, ultraviolet and seawater aging enhanced its retention in the substrate. The retention percentages of both PE1 and PTFE in the column increased with the elevated ionic strength and the decrease of fluctuation velocity. This work highlights that capillary fringe fluctuation can serve as a pathway to relocate MPs to the tidal aquifer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".