Modelling reduction and enrichment effects of urban stormwater best management practices on phosphorus at the watershed scale
Bibliographic record
Abstract
A variety of best management practices (BMPs) are being implemented to attenuate the increasing eutrophication risk caused by excessive phosphorus (P) export via urban stormwater runoff. However, P reduction performance of the BMPs are highly variable under different climatic, watershed, and design settings. Many of BMPs are actually reported to enrich P concentrations, which questions their efficiency to reduce P load export from urban watersheds. In this study, we developed a data-driven machine learning model to predict P reduction or enrichment in urban stormwater BMPs. The model is trained and validated using hydrologic and P concentration data for several typical urban stormwater BMPs, including traditional systems (retention pond, wetland basin and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale and grass strip), from the International Stormwater BMP Database. Unlike other models in previous studies, our model can simulate both P reduction and enrichment by urban BMPs under specific input and climatic, watershed, and BMP design conditions. Additionally, a PCSWMM (Stormwater Management Model) was developed for a small urban watershed in Southern Ontario (Lake Wilcox (LW) watershed) to provide representation of the rainfall–runoff processes in the watershed. Parameters obtained by PCSWMM were calibrated using the observed data, and these will be used along with data-driven BMP P model for LW to estimate projected changes in P export under different BMPs application and climatic scenarios at the watershed scale. This study will propose an innovative and more robust method to estimate attenuation of P export by BMPs at watershed scale. It will also improve our understanding about critical climatic, watershed and BMP design variables that control BMPs P reduction performances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".