Experience with High-Rate Treatment of Stormwater in a Pilot Installation in Toronto, Ontario, Canada
Bibliographic record
Abstract
Treatment of urban stormwater by clarification, with and without flocculant addition, was investigated in Toronto, Ontario, Canada using a pilot-scale clarifier with removable lamellar plates. Almost 90 stormwater runoff events were characterized at the study site and found significantly polluted, in comparison to the U.S. NURP median site data. Earlier research results indicated good treatability of this stormwater by lamellar clarification with flocculant addition (total suspended solids, TSS, removal of 83%, at a total vessel surface-loading rate of 15–35 m/h), but there were concerns about laborious plate cleaning after storm events. With the aid of numerical modeling, hydraulic improvements to the clarifier inlet zone were retrofitted in 2004, which permitted the removal of the lamellar pack without a significant loss in treatment efficiency. In the modified clarifier without lamellas, addition of a cationic polymeric flocculant at 4 mg/L provided a TSS removal of 77%, at surface-loading rates up to 43 m/h. The use of the polymer did not increase the acute toxicity of the process effluent. The stormwater sediment and clarifier sludge at this site were severely polluted by several heavy metals (Cu, Mn, Zn), according to the Ontario aquatic sediment quality guidelines, and the clarifier sludge would require special disposal considerations. The treatment process tested appears to be applicable in projects requiring intensive stormwater treatment at compact sites.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".