Infiltration of Forest Fire and Wood Smoke: An Intervention Study to Assess Air Cleaner Effectiveness
Bibliographic record
Abstract
P-289 Introduction: Forest fires and residential wood-burning are significant sources of air pollution. PM2.5 from these sources has been linked to adverse health effects. Consequently, exposed populations should be provided with evidence-based recommendations on exposure reduction. Recommendations typically include remaining indoors and use of air cleaners, yet little information is available on PM infiltration during smoke episodes or on effectiveness of air cleaners in reducing indoor concentrations. Specific research objectives of this study were to measure indoor infiltration of outdoor PM2.5 from forest fires/residential wood-smoke and to determine effectiveness of High Efficiency Particulate Air (HEPA) filter air cleaners in reducing indoor PM2.5. Methods: Indoor and outdoor PM2.5 sampling was conducted over a 48-hour period in 21 homes during winter and in 16 homes during summer. Winter sampling was conducted in 2004 in a northern Canadian community affected by residential wood-burning. Summer sampling was conducted in 2004 and 2005 in southern British Columbia, Canada in communities impacted by forest fire smoke. Continuous 1-minute averages using a light-scattering device (personal DataRAM) and integrated 48-hour filter samples (Harvard Impactor) of PM were collected for each home. A portable HEPA filter air cleaner was operated indoors during the entire sampling period, however the filter was removed for 1 of the 2 days. Infiltration factors (Finf) were calculated using a recursive model (Switzer and Ott. 2001) after removal of indoor-generated PM2.5 data using censoring algorithms (Allen et al. 2003). Results: Valid 48 hour samples were obtained from 19 homes in winter and 12 homes in summer. Average wintertime PM2.5 concentrations were 14.2ug/m3 and 35.6ug/m3 for indoor and outdoor measurements respectively. Average summer concentrations were 12.3ug/m3 and 29.2ug/m3 for indoor and outdoor measurements respectively. For days when the filter was not used, mean Finf± SD values of 0.29±0.20 and 0.53±0.32 were found for winter and summer homes respectively. On days with filter use, Finf± SD was lower in both seasons with values of 0.11±0.10 and 0.22±0.24 for winter and summer respectively. Mean± SD air cleaner efficiencies ([Finf without filter - Finf with filter]/ Finf without filter) in summer and winter were 74±40% and 49±41% respectively. Effects of housing characteristics on infiltration and air cleaner efficiency based on multivariate models will also be presented. Discussion and Conclusions: For exposures to wood and forest fire smoke, infiltration was higher in summer versus winter and in both seasons air cleaners were effective in lowering Finf.
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 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 | 0.003 | 0.001 |
| 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) | 0.000 | 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".