Occurrence and Gas–Particle Partitioning of Organic UV-Filters in Urban Air
Bibliographic record
Abstract
. Homosalate (HMS) was the most prevalent organic UV-filter in air (47% of the total concentration), followed by 2-ethylhexyl salicylate (EHS, ∼29%), E- and Z-2-ethylhexyl 4-methoxycinnamate (EHMC, ∼17%). Ambient air (gas + particle phase) concentrations of organic UV-filters showed a strong seasonality, with peak levels during the summer. An analysis of Clausius-Clapeyron slopes indicated that much of the ambient burden of organic UV-filters are explained by volatilization from terrestrial and aquatic surfaces and supplemented with human activities and use of lotions and sunscreens, containing organic UV-filters, in addition to its use in plastics, textiles, paints, and pesticides. The results showed that organic UV-filters exist mainly in the gas phase with some exceptions, for instance, octocrylene (OCR), which was associated with both gas and particle phases, and avobenzone (AVB), which was predominantly in the particle phase. Lastly, this study revealed the need for basic physical chemical property data for organic UV-filters, including information on transformation rates and products, for better evaluating their environmental fate and effects.
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 | 0.001 | 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) | 0.001 | 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".