Partitioning of removal of microplastics and other anthropogenic particles among influent, biosolids and final effluent in a tertiary wastewater treatment plant
Bibliographic record
Abstract
Abstract Influent, final effluent, biosolids, ultrafiltration membrane module inflow and outflow samples were collected in a tertiary wastewater treatment plant to enumerate and categorize anthropogenic particles, including microplastics in the 250 µm - 5 mm size fraction. Samples were digested with 50% hydrogen peroxide, filtered, and examined via microscopy at 40-50x magnification. Most (99%) anthropogenic particles were present in biosolids compared to final effluent (<1%), with fibres numerically dominating influent and final effluent, and fragments dominating biosolids and ultrafiltration inflow and outflow. Assessment of ultrafiltration (pore size = 0.02 µm) inflow and outflow before and after membrane deep cleaning and repair revealed high variability in source abundance and a general, yet non-significant reduction in particle counts in module permeate over 20 weeks. Removal techniques require assessment to explore effective options to mitigate the potential impacts, particularly to freshwater environments, of anthropogenic microparticle release. Our data reveal the utility of quantification and removal options for anthropogenic particles in wastewater treatment plants as they are conduits between municipal and industrial wastewater sources and freshwater and terrestrial receiving environments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".