Stormwater Bioretention Cells Are Not an Effective Treatment for Persistent and Mobile Organic Compounds (PMOCs)
Bibliographic record
Abstract
Bioretention cells are a stormwater management technology intended to reduce the quantity of water entering receiving bodies. They are also used to reduce contaminant releases, but their performance is unclear for hydrophilic persistent and mobile organic compounds (PMOCs). We developed a novel eight-compartment one-dimensional (1D) multimedia model of a bioretention cell (“Bioretention Blues”) and applied it to a spike and recovery experiment conducted on a system near Toronto, Canada, involving PMOC benzotriazole and four organophosphate esters (OPEs). Compounds with (log D OC ) (organic carbon-water distribution coefficients) < ∼2.7 advected through the system, resulting in infiltration or underdrain flow. Compounds with log D OC > 3.8 were mostly sorbed to the soil, where subsequent fate depended on transformation. For compounds with 2.7 ≤ log D OC ≤ 3.8, sorption was sensitive to event size and compound-specific diffusion parameters, with more sorption expected for smaller rain events and for compounds with larger diffusion coefficients. Volatilization losses were minimal for all compounds tested. Direct uptake by vegetation also played a negligible role regardless of the compounds’ physicochemical properties. Nonetheless, model simulations showed that vegetation could play a role by increasing transpiration, thereby increasing sorption to the bioretention soil and reducing PMOC release. Model results suggest design modifications to bioretention cells.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".