Impact of influent wastewater quality on nitrogen removal rates in multistage treatment wetlands
Bibliographic record
Abstract
Nitrogen removal in treatment wetlands is influenced by many factors, and the presence of electron donors (biodegradable organic matter) and electron acceptors (nitrate ions) is the main limiting one; for obtaining these conditions, multistage treatment wetlands (MTWs) are required, where an extensive nitrification can be obtained in the first stages under aerobic conditions leaving then to the following anoxic/anaerobic stages the duty of the denitrification. Most of the biodegradable organic matter is however oxidised in the first stages, and therefore, the inlet to the denitrification beds is usually poor of easily degradable carbon sources. This study is comparing the long-term performances obtained at several MTWs operating in Europe (North and South) and North Africa in order to understand if there is a significant avail in making use of the influent chemical oxygen demand (COD)/N ratio during the design phase for ensuring proper performances in terms of N overall removal. The statistic analysis performed in this study have shown that MTWs are capable to ensure sufficient removal of both organic and nutrients even in unfavourable proportions of macronutrients (C and N). The usual assumptions for conventional biological treatment systems concerning adequate C/N ratios seem to be dubious in case of wastewater treatment in MTWs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".