Efficiency of microplastics removal in selected wastewater treatment plants – preliminary studies
Bibliographic record
Abstract
ABSTRACT Recently more and more attention is being paid to pollution of the environment by microplastics. The problem is well recognized in marine and surface water in Western Europe. There are also well-documented studies on removal of microplastics during wastewater treatment in Germany, Finland, Denmark, Canada and Norway. So far the problem has not been identified in Eastern European countries, including Poland. Because of this, it is of high importance to evaluate the scale of problem also in these countries. The paper presents the results of preliminary studies on microplastics content in influents, effluents and sewage sludge of selected wastewater treatment plants in southern Poland. It was stated that content of microplastics in influents was in the range from 19.4 · 10 3 to 552.2 · 10 3 particles per 1 m 3 , in effluents from 28 to 960 particles per 1 m 3 . Microlitter particles removed from raw wastewater were accumulated in sewage sludge. Total concentration of microplastic particles in the sludge was in the range from 6.7 · 10 3 to 62.6 · 10 3 particles per 1 kg d.m. In liquid phase, finer fractions of microplastics were dominant. In sewage sludge larger particles, especially fibers, were effectively cumulated. About 95%–99% removal efficiency of microplastics from influents was stated. No correlation has been found between the wastewater flow rate and the content of microplastics in influent, these problems require, however, more detailed analysis of microplastics chemical composition and mass.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".