Ultrafine and Fine Particulate Matter Levels over Resurfacing Operations at Skating Arenas
Bibliographic record
Abstract
Background: Ice arenas are a unique public indoor space where there is extensive use of combustion engines combined with an exercising population. This pilot study seeks to identify ultrafine particle (UFP) levels and fine particulate matter (PM2.5) in indoor ice arenas across resurfacing operations. Ice making is a time-consuming and expensive process. In order to preserve the ice and ensure a smooth surface, arena operators resurface ice based on usage. Ice resurfacing involves driving a resurfacing machine over the surface of the ice as it simultaneously scrapes off a thin layer of ice and fills scratches with water, resulting in a smooth and even ice surface. Pollutants can be trapped near the ice from stagnant air flow due to a combination of boards and glass surrounding the ice and a thermal inversion which keeps colder air near the ice surface.Methods: UFP and PM2.5 measurements were continually collected using a TSI P-Trak and a TSI Dusttrak DRX. Samples were run indoors before, during and for approximately 20 minutes after one resurfacing event to capture changes in particle levels throughout the event and outdoor background measurements were taken. Sampling was conducted on two separate days in 12 arenas, once in the morning and once in the afternoon. Arenas were selected that differed in their ventilation characteristics and resurfacing equipment (7-propane, 3-natural gas, 1-gasoline, 1 electric).Results: Mean resurfacing time was 9.1 minutes (range 6-16) and was performed hourly during high arena usage. Peaks in UFP were typically observed 10 minutes post resurfacing with a mean increase in peak UFP from pre- to post- resurfacing across arenas of 65% (range 13-400%). However, PM2.5 levels were not elevated following resurfacing operations.Conclusion: The poster will provide results on changes in UFP levels by fuel type of resurfacer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".