Modeling multi-year phosphorus dynamics in a bioretention cell: phosphorus partitioning, accumulation, and export
Bibliographic record
Abstract
Abstract: Nutrient phosphorus (P) export from urban areas via stormwater runoff contributes to eutrophication of downstream aquatic ecosystems. Bioretention cells are a Low Impact Development (LID) technology promoted as a green solution to attenuate urban peak flow discharge, as well as the export of excess nutrients and other contaminants. Despite their rapidly growing implementation worldwide, a predictive understanding of the efficiency of bioretention cells in reducing P runoff remains limited. Here, we present a reaction-transport model data and codes that was used to simulate the fate and transport of P in a bioretention cell facility in the greater Toronto metropolitan area. The model incorporates a representation of the biogeochemical reaction network that controls P cycling within the cell. We used the model as a diagnostic tool to determine the relative importance of processes immobilizing P in the bioretention cell. The model predictions were compared to multi-year observational data on 1) the outflow loads of total P (TP) and soluble reactive P (SRP) during the 2012-2017 period, 2) TP depth profiles collected at 4 time points during the 2012-2019 period, and 3) sequential chemical P extractions performed on core samples from the filter media layer obtained in 2019. According to the modeling results, groundwater recharge was principally responsible for decreasing the surface water discharge from the bioretention cell (63% runoff reduction). From 2012 to 2017, the cumulative outflow export loads of TP and SRP only accounted for 1% and 2% of the corresponding inflow loads, respectively. Accumulation in the filter media layer was the predominant mechanism responsible for the reduction in P outflow loading (57% retention of TP inflow load) followed by plant uptake (21% TP retention). Of the P retained within the filter media layer, 48% occurred in stable, 41% in potentially mobilizable, and 11% in easily mobilizable forms. There were no signs that the P retention capacity of the bioretention cell would approach saturation in the near future. The design of this bioretention facility seems therefore especially efficient at controlling urban P runoff. This Dataset includes the model scripts and modelled results dataset for Elm Drive bio retention 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".