Breath Biomarkers to Measure Uptake of Volatile Organic Compounds by Bicyclists
Bibliographic record
Abstract
Breath biomarkers were used to study uptake of traffic-related volatile organic compounds (VOCs) from urban bicycling. Breath analysis was selected because it is one of the least invasive methods to assess urban traveler exposure. Research hurdles that were overcome included considering that factors other than on-road exposure can influence concentrations in the body, and absorbed doses during a trip can be small compared to baseline body burdens. Pre-trip, on-road, and post-trip breath concentrations and ambient air concentrations were determined for 26 VOCs for bicyclists traveling on different path types. Statistical analyses of the concentration data identified eight monoaromatic hydrocarbons potentially useful as breath biomarkers to compare differences in body levels brought about by urban travel choices. Breath concentrations of the biomarker compounds were significantly higher than background levels after riding on high-traffic arterial streets and on a path through a high-exposure industrial area, but not after riding on low-traffic local streets or on other off-street paths. Modeled effects of high-traffic streets on ambient concentrations were 100-200% larger than those of low-traffic streets; modeled effects of high-traffic streets on breath concentrations were 40-100% larger than those of low-traffic streets. Similar percentage increases in breath concentrations are expected for bicyclists in other cities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".