Ultrafine and Fine Particulate Matter Levels over Resurfacing Operations at Skating Arenas
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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 it