Between source and sea: The role of wastewater treatment in reducing marine microplastics
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are a focal point for the removal of microplastic (MP) particles before they are discharged into aquatic environments. WWTPs are capable of removing substantial quantities of larger MP particles but are inefficient in removing particles with any one dimension of less than 100 μm, with influents and effluents tending to have similar quantities of these smaller particles. As a single WWTP may release >100 billion MP particles annually, collectively WWTPs are significant contributors to the problem of MP pollution of global surface waters. Currently, there are no policies or regulations requiring the removal of MPs during wastewater treatment, but as concern about MP pollution grows, the potential for wastewater technologies to capture particles before they reach surface waters has begun to attract attention. There are promising technologies in various stages of development that may improve the removal of MP particles from wastewater. Better incentivization could speed up the research, development and adoption of innovative practices. This paper describes the current state of knowledge regarding MPs, wastewater and relevant policies that could influence the development and deployment of new technologies within WWTPs. We review existing technologies for capturing very small MP particles and examine new developments that may have the potential to overcome the shortcomings of existing methods. The types of collaborations needed to encourage and incentivize innovation within the wastewater sector are also discussed, specifically strong partnerships among scientific and engineering researchers, industry stakeholders, and policy decision makers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".