Water Quality Impacts of Green Roofs Compared with Other Vegetated Sites
Bibliographic record
Abstract
Green roofs are a convenient stormwater management strategy in highly developed, urbanized areas. Research has demonstrated that green roofs are effective at mitigating stormwater runoff volume, in addition to providing a range of other environmental benefits. Previous studies on the overflow from extensive green roofs have indicated that the overflow from these roofs, particularly those maintained with fertilizer, may contribute nutrients to nearby streams, storm sewers, and adjacent waterways. Whereas many studies have compared green roof nutrient concentrations with that of conventional roofs and urban streams, few studies have compared green roof effluent with other vegetated systems’ effluent. In this study, located in Villanova, Pennsylvania, green roof effluent was evaluated and compared with vegetated land uses (e.g., woods and grass) and other stormwater control measures (e.g., bioinfiltration rain garden and constructed stormwater wetland) typically found in urban watersheds. Effluent samples from all sites were tested and analyzed for concentration and mass loading of nitrogen (nitrate, nitrite, total Kjeldahl nitrogen, and total nitrogen) and phosphorus (orthophosphate and phosphorus). Overall, the green roof effluent concentrations for nitrogen and phosphorus species were statistically different than the other land uses, and often with higher concentrations. The green roof effluent was most statistically similar to the wooded land use. From a mass loading perspective, in terms of unit area of the contributing watershed, the green roof had a higher loading than the other land uses. However, the fertilized green roof exported less than 15 percent of the total input mass of nitrogen and phosphorus, demonstrating that volume reduction aids in managing the effluent.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".