Evaluation of Green Roofs for Runoff Retention, Runoff Quality, and Leachability
Bibliographic record
Abstract
Abstract This three-year study evaluates the quantity and quality of runoff from an extensive green roof on a multistory building in Toronto. Laboratory physical, chemical, and leachate analyses of eleven commercially available green roof growing media were also undertaken to help identify the potential influence that the growing media may have on runoff chemistry. Continuous precipitation and runoff data collected over 18 months outside of the winter period indicated that the green roof discharged 63% less runoff than a neighbouring conventional modified bitumen roof. Runoff volumes from the green roof averaged 42% less than the conventional roof in April and November, and between 70 and 93% less during the summer months. Water samples were collected from both roofs during 21 rain events in 2003 and 2004 and analyzed for general chemistry (e.g., pH, total suspended solids), metals, nutrients, bacteria (n = 16), and polycyclic aromatic hydrocarbons (n = 18). Loads of most chemical variables in green roof runoff were lower than from the conventional roof. Exceptions included constituents such as calcium, magnesium, and total phosphorus, which were either naturally present in the media or were added to promote plant growth. Total phosphorus concentrations in green roof runoff were significantly higher than the conventional roof (α = 0.001), and regularly exceeded the Ontario receiving water objective (0.03 mg/L). Phosphorus concentrations fell significantly after the first year of monitoring (α = 0.001), suggesting that the nutrient is being leached from the media. Chemical analyses of green roof growing media showed that levels of most constituents were similar to or lower than typical background concentrations for agricultural soils in Ontario. However, leachate concentrations from several media exceeded receiving water standards for phosphorus, aluminum, copper, iron, and vanadium. This study highlights the importance of engineering green roof media to minimize leaching of nutrients and other contaminants while maintaining their ability to support plant growth.
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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 | 0.001 | 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".