Infiltration of Forest Fire and Wood Smoke: An Intervention Study to Assess Air Cleaner Effectiveness
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».