Nitrogen dynamics in a constructed wetland system treating landfill leachate
Bibliographic record
Abstract
A pilot scale treatment system was established in 2002 at the Laflèche Landfill in Eastern Ontario, Canada. The system consists of a series of treatment steps: a stabilisation basin (10,000 m3), a woodland peat trickling filter (5,200 m2), a subsurface flow constructed wetland planted in Phragmites sp. (2,600 m2), a surface flow constructed wetland planted in Typha sp. (3,600 m2) and a polishing pond (3,600 m2). The system operates from May to December with leachate being recycled within the landfill during the winter months. Hydraulic loading was increased three-fold over four operating seasons with nitrogen and organic mass loading increasing six-fold. Excellent removal efficiencies were observed with 93% BOD5, 90% TKN and 97% NH4-N removed under the highest loading conditions. Almost complete denitrification was observed throughout the treatment system with NO3-N concentrations never exceeding 5mg L(-1). The peat filter reached treatment capacity at a hydraulic loading of 4cm d(-1) and organic loading rate of 42 kg BOD ha(-1) d(-1), which is consistent with design criteria for vertical flow wetland systems and intermittent sand filters, The first order plug flow kinetic model was effective at describing TKN and ammonium removal in the SSF and FWS wetlands when background concentrations were taken into account. Ammonium removal k-values were consistent with the literature at 52.6 and 57.7 yr(-1) for the SSF and FWS wetlands, respectively, while TKN k-values at 6.9 and 7.7 yr(-1) were almost an order of magnitude lower than literature values, suggesting that leachate TKN could contain refractory organics not found in domestic wastewater.
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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 | 0.000 | 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".