Technical Note — The Use of a Very Large Constructed Sub-Surface Flow Wetland to Treat Glycol-Contaminated Stormwater from Aircraft De-Icing Operations
Bibliographic record
Abstract
Abstract All of the pollutants found in stormwater runoff at airports, including surface and aircraft de-icing/anti-icing glycols, can be treated and removed to low levels in well-designed sub-surface flow (SSF) constructed wetland systems. There are two common forms of constructed wetlands used for pollution control: those where water flows over the surface among wetland plants (free water surface or marsh type wetlands); and SSF types where the wastewater flows below the normally dry surface of a gravel substrate in which the wetland plants grow. SSF wetlands have no open water to attract waterfowl and are particularly suitable for use at airports. Of the glycol used at Edmonton International Airport (EIA), 80 to 90% eventually entered surface runoff. Edmonton International Airport's operator, the Edmonton Regional Airports Authority (Edmonton Airports) evaluated a number of glycol management options, including constructed wetlands. As a result, a very large SSF wetland system was installed to handle glycol-contaminated stormwater. This paper reviews results of a feasibility study carried out to define design parameters and scale up kinetics for this wetland system, the detailed design that resulted, the SSF wetland's construction, and the start-up of the Edmonton facilities in August of 2000. It also compares the Edmonton wetland system with a similar facility at Heathrow Airport in the United Kingdom.
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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 | 0.000 | 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".