Benzene Photolysis on Ice: Implications for the Fate of Organic Contaminants in the Winter
Bibliographic record
Abstract
The members of the important class of organic pollutants known as BTEX (benzene, toluene, ethylbenzene, and xylenes) do not undergo direct photolysis in natural waters, because their absorption spectra do not overlap that of the solar radiation which reaches the Earth's surface. Recent work has shown that aromatic compounds undergo significant red-shifts in their absorption spectra when they are present at air-ice interfaces, suggesting that BTEX components could undergo direct photolysis at ice surfaces. Using glancing-angle laser-induced fluorescence (LIF) as a probe, we measured benzene photodegradation at lambda > 295 nm having a rate constant of (3 +/- 1 x 10(-4) s(-1)) under our experimental conditions. We predict that the photolysis rate at environmental ice surfaces will be similar, based on the photon flux dependence we measured. This study presents the first report of direct benzene photolysis under environmentally relevant conditions. The results suggest that direct photolysis could be an important removal pathway for organic pollutants such as BTEX in snow-covered regions, for example, in polar or urban areas contaminated by oil spills or leaks.
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 | 0.001 | 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".