Field assessment of thermal conditions in naturally ventilated classrooms during spring: microclimate and passive cooling impacts in cold climate
Notice bibliographique
Résumé
Purpose Previous studies on climate change’s impact on indoor thermal conditions have largely focused on summer, with little attention to spring and fall, which were historically considered comfortable. This raises concerns about how older buildings perform during these seasons. Many studies have used computer simulations or short-term field measurements, but seasonal field data is limited. This paper aims to (1) conduct field measurements of outdoor air temperature in three city locations to analyze microclimate effects, (2) analyze indoor air temperature data from 10 classrooms in five school buildings during spring 2021 and (3) assess how existing passive buildings impact indoor air temperature. Design/methodology/approach A comprehensive methodology is developed to achieve the paper’s objectives. It begins with a selection approach using three filtering criteria to identify schools at high risk of overheating. Microclimatic variations are then analyzed by installing rooftop weather stations at three sites to monitor conditions during spring and summer, aiming to assess the effects of climate change. Finally, indoor air temperatures are monitored in the warmest and coldest classrooms, chosen based on similar physical characteristics. The effectiveness of passive cooling strategies is evaluated through comparative and statistical analyses during both occupied spring periods and unoccupied summer breaks to assess seasonal and operational impacts. Findings The methodology identified five comparative school buildings out of 396 in Montreal. Key findings show that spring, once considered cold in Canada, is hot, with three heat waves recorded in spring 2021, more than in summer. Notable outdoor temperature differences across locations revealed strong microclimatic effects. Significant indoor thermal variation was observed within the same buildings, with the warmest classrooms up to 1.5°C hotter than the coolest. Classroom temperatures reached peaks of 32°C. The effectiveness of passive cooling strategies varied by season. During heatwaves, indoor temperatures peaked higher in HW3 (30°C), indicating intense discomfort, while HW2 showed the highest maximum temperatures (31°C). Originality/value This study provides original work into the evolving thermal conditions of school buildings in a cold climate, emphasizing the overlooked impact of springtime heatwaves. By integrating extensive field measurements with microclimate analysis, it enhances understanding of indoor overheating risks and the role of key building parameters. The findings contribute to climate-responsive building design, particularly for naturally ventilated classrooms. Artificial intelligence may be used solely for refining the writing, ensuring clarity and precision without altering the originality, methodology or scientific contributions of the research.
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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 | 0,000 | 0,000 |
| 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 ».