Phosphorus accumulation in a bioretention cell in Mississauga, Ontario: Insights from field monitoring and process-based modeling
Bibliographic record
Abstract
Bioretention cells are a Low Impact Development (LID) technology that is being promoted as a green solution to attenuate urban stormwater nutrient loadings. Despite extensive implementation of bioretention cells in Canada, the mechanistic understanding of phosphorus (P) cycling in bioretention cells is still limited. We conducted detailed analyses of (geo)chemical and hydrological data coupled to numerical reactive transport modeling to simulate the fate and transport of P in a bioretention cell located in Mississauga (Ontario, Canada) within the Credit River watershed. Our objective is to utilize the model to predictively understand the accumulation and speciation of P in the bioretention cell under long-term field operation. Unlike existing bioretention models, our model incorporates a detailed representation of the biogeochemical processes that control P cycling in the bioretention cell. We further compare the model predictions with data from sequential chemical extractions of P from soil samples taken from the bioretention cell. The model correctly estimates the cumulative TP (total P) and SRP (soluble reactive P) outflow loadings from the bioretention cell, as well as the TP accumulation rate and observed partitioning of P over the different pools in the bioretention cell. The relative importance of various processes controlling P retention are assessed using mass balance calculations and sensitivity analyses of the model. The results show that filtration of fine P-containing particles and slow sorption are the main processes retaining P in the bioretention cell.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".