Trace Organic Contaminant Transfer and Transformation in Bioretention Cells: A Field Tracer Test with Benzotriazole
Bibliographic record
Abstract
Bioretention cells can effectively infiltrate stormwater runoff and partly remove conventional water contaminants. A field tracer injection experiment in a conventionally designed bioretention cell was used to investigate the fate of benzotriazole, a model trace organic contaminant, during and between runoff events. Moderate (29%) benzotriazole load reductions were measured during the 6 h long injection experiment. The detection of 1-methyl benzotriazole, hydroxy benzotriazole, and methoxy benzotriazole provided in situ evidence of some rapid benzotriazole microbial transformation during the tracer test and more importantly between the events. The detection of benzotriazole alanine and benzotriazole acetyl alanine also showed fast benzotriazole phytotransformation to amino acid conjugates during the tracer test and suggests further transformation of phytotransformation products between events. These data provide conclusive full-scale evidence of benzotriazole microbial and phytotransformation in bioretention cells. Non-target chemical analysis revealed the presence of a diverse range of trace organic contaminants in urban runoff and exiting the bioretention cell, including pesticides and industrial, household, and pharmaceutical compounds. We have demonstrated the in situ potential of urban green infrastructure such as bioretention cells to eliminate polar trace organic contaminants from stormwater. However, targeted design and operation strategies, for example, hydraulic control and the use of soil amendments, should be incorporated for improved bioretention cell performance for such compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".