Bioretention Design Modifications Increase the Simulated Capture of Hydrophobic and Hydrophilic Trace Organic Compounds
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Stormwater rapidly moves trace organic contaminants (TrOCs) from the built environment to the aquatic environment. Bioretention cells reduce loadings of some TrOCs, but they struggle with hydrophilic compounds. Herein, we assessed the potential to enhance TrOC removal via changes in bioretention system design by simulating the fate of seven high-priority stormwater TrOCs (e.g., PFOA, 6PPD-quinone, PAHs) with log K OC values between −1.5 and 6.74 in a bioretention cell. We evaluated eight design and management interventions for three illustrative use cases representing a highway, a residential area, and an airport. We suggest two metrics of performance: mass advected to the sewer network, which poses an acute risk to aquatic ecosystems, and total mass advected from the system, which poses a longer-term risk for persistent compounds. The optimized designs for each use case reduced effluent loadings of all but the most polar compound (PFOA) to <5% of influent mass. Our results suggest that having the largest possible system area allowed bioretention systems to provide benefits during larger events, which improved performance for all compounds. To improve performance for the most hydrophilic TrOCs, an amendment like biochar was necessary; field-scale research is needed to confirm this result. Our results showed that changing the design of bioretention systems can allow them to effectively capture TrOCs with a wide range of physicochemical properties, protecting human health and aquatic species from chemical impacts.
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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 | 0.000 |
| 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".