Bioretention Cells Provide a 10-Fold Reduction in 6PPD-Quinone Mass Loadings to Receiving Waters: Evidence from a Field Experiment and Modeling
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Road runoff to streams and rivers exposes aquatic organisms to complex mixtures of chemical contaminants. In particular, the tire-derived chemical 6PPD-quinone ( N -(1,3-dimethylbutyl)- N ′-phenyl- p -phenylenediamine-quinone) is acutely toxic to several species of salmonids, which are critical to fisheries, ecosystems, and Indigenous cultures. We therefore urgently require interventions that can reduce loadings of 6PPD-quinone to salmonid habitats. Herein, we conducted a spike and recovery experiment on a full-scale, mature bioretention cell to assess the efficacy of stormwater green infrastructure technologies in reducing 6PPD-quinone loadings to receiving waters. We then interpreted and extended the results of our experiment using an improved version of the “Bioretention Blues” contaminant transport and fate model. Overall, our results showed that stormwater bioretention systems can effectively mitigate >∼90% of 6PPD-quinone loadings to streams under most “typical” storm conditions (i.e., < 2-year return period). We therefore recommend that stormwater managers and other environmental stewards redirect stormwater away from receiving waters and into engineered green infrastructure systems such as 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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".