Field assessment of thermal conditions in naturally ventilated classrooms during spring: microclimate and passive cooling impacts in cold climate
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".