Removal of Microplastics/Microfibers and Detergents from Laundry Wastewater by Microbubble Flotation
Bibliographic record
Abstract
Microplastics (MPs), particularly microplastic fibers (MFs), released during laundry processes, constitute a major source of primary MPs in the water environment, raising growing ecological and environmental concerns. This study developed and evaluated a microbubble-enhanced flotation approach to effectively remove MPs/MFs and surfactants─essential components of commercial detergents and common pollutants─from laundry wastewater (LW). Through bench-scale and pilot-scale experiments, we investigated a wide range of parameters affecting recovery efficiency, focusing on MP properties (5 plastic types, 3 particle size ranges, and 4 concentration levels), water chemistry (5 detergent concentrations), and operational conditions (2 types of gases, 3 bubble size ranges, and 3 gas flow rates). Our results showed that under optimized conditions, microbubble flotation could effectively remove over 98 wt % of MPs/MFs and over 95 wt % of surfactants from LW. Moreover, the high removal rates achieved in bench-scale microbubble flotation processes were successfully reproduced in upscaling trials using a pilot-scale bubble column of 5.7 m in height. This work demonstrates the robustness and reliability of microbubble flotation for industrial LW treatment, providing a straightforward, cost-effective, and environmentally friendly solution for the concurrent removal of MPs and surfactants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".