Impact of Wildfire Smoke PM2.5 on Indoor Air Quality of Public Buildings on a University Campus
Bibliographic record
Abstract
With increasing wildfire events impacting many regions worldwide, understanding and mitigating the effects of wildfire smoke on indoor air quality (IAQ) in public buildings are essential for protecting occupant health. This study investigated the impact of wildfire smoke on the IAQ across 24 campus buildings in Alberta, Canada, representing public spaces with varied ventilation systems. Using a network of low-cost sensors to monitor indoor PM 2.5, the study identified significant spikes during wildfire smoke events, with 71% of buildings exceeding the Canadian Ambient Air Quality Standards daily limit of 27 μg/m 3 . The buildings had mechanical ventilation systems with filters with different Minimum Efficiency Reporting Value (MERV) ratings. MERV13 filters were found to be more efficient at capturing PM 2.5 particles, resulting in lower indoor/outdoor PM 2.5 ratios (0.12 ± 0.07) compared to MERV8 filters (0.28 ± 0.14). Buildings with air change rates (ACH) ranging from 5 to 15 per hour exhibited different infiltration patterns, with higher ACH generally leading to elevated indoor PM 2.5 concentrations during wildfire events. This highlights the need to balance ventilation and pollutant infiltration by optimizing ACH rates and filtration efficiency to reduce indoor PM 2.5 . The trajectory-fire interception method, combined with satellite data, enhanced the identification of wildfire-influenced periods, contributing to a better understanding of smoke infiltration dynamics. These findings underscore that even advanced filtration and ventilation systems alone may not ensure a healthy IAQ during extreme pollution. Real-time pollutant measurements are crucial for effective IAQ management. The findings offer valuable insights for building administrators and policymakers, helping them develop strategies to mitigate the effects of wildfire smoke and to support healthier indoor environments during wildfire seasons.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".