Reducing Phosphorus in Urban Stormwater Runoff with Low Impact Development
Bibliographic record
Abstract
Stormwater runoff has been identified as a major source of water pollution within the Lake Simcoe Basin in Ontario, Canada. Stormwater is estimated to contribute approximately 1/3 of phosphorus (P) load within Lake Simcoe and its surrounding tributaries. The effects of stormwater on the water quality of Lake Simcoe are expected to become increasingly significant as urban development continues to expand. Traditional conveyance and end-of-pipe stormwater management is no longer achieving the desired standard of watershed management within this area. Low impact development (LID) is emerging as a possible alternative which may allow hydrologic and environmental objectives to be achieved. LID practices can greatly reduce urban runoff, restore more naturalized hydrographs, and delay costly replacements of aging infrastructure. However the effluent of some LID practices, such as bioretention or green roofs, may have higher concentrations of P. Even so the reduction in overall urban runoff rates due to LID infrastructure may result in a net-decrease P loading. A study to quantify the extent to which P loading may be reduced through the introduction of LID techniques in selected urban drainage areas within the Lake Simcoe Basin is currently underway at the University of Guelph. Urban catchments in the East Holland Subwatershed will be utilized for the investigation. Several LID scenarios comprising different combinations of LID practices (e.g. porous pavement, green roofs, bioretention) will be assessed. The analysis will be based on ranges of runoff volume reductions and effluent P concentrations attributed to various LID techniques in the literature. P loading reductions achievable with various combinations of LID practices will be quantified using water and mass balance approaches. This paper provides an outline of the preliminary methodology and will demonstrate an application of the mass-balance model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".