From hotspots to background: High-resolution mapping of ethylene oxide in urban air
Bibliographic record
Abstract
Ethylene oxide (EtO) is a human carcinogen whose release from sterilization facilities to ambient air has gained recent attention. Measurements have typically relied on canister samples collected over minutes to hours and analyzed using gas chromatography/mass spectrometry (GC/MS). A novel application of tunable infrared laser direct absorption spectrometry (TILDAS) was recently deployed aboard a mobile air quality laboratory in Toronto, Canada, to measure EtO in near real-time. Detection limits comparable to canister-GC/MS methods were achieved while stationary at averaging times of only 100 s, and high frequency (1-s) sampling detected EtO at several locations. EtO was observed consistently near the only local facility reporting on-site releases to Canada's National Pollutant Release Inventory. The maximum 1-s mixing ratio observed nearby was 18 ppb (33 μg m−3), and a mean 1-s mixing ratio of 0.43 ppb (0.78 μg m−3) was observed in the vicinity of the detectable plumes that covered industrial, commercial and residential land uses up to 900 m downwind. EtO in those plumes was not related to common air pollutants such as nitric oxide (NO), carbon monoxide (CO), or methane (CH4), whereas it was paired with elevated NO or CO and CH4 at some other locations. EtO was less abundant elsewhere in the city, and no detectable EtO was found near potential sources including hospitals, spice distributors and vehicle exhaust. The EtO background measured in wintertime Toronto air was indistinguishable from zero and substantially lower than that reported in studies using canister-GC/MS methods. The multi-pollutant mobile method described herein marks a significant step forward in the capability to characterize atmospherically relevant concentrations of EtO at high spatiotemporal resolution. Future applications of high-resolution techniques will allow for comprehensive investigations of source impacts and characterization of ambient levels for which deployment of conventional canister/GC-MS methods would be unsuitable or prohibitive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".