Evaluation of indoor environmental quality in Sagkeeng Junior School
Notice bibliographique
Résumé
This research involved investigating the effects of a First Nations school’s indoor environmental quality on health and human performance. This research project was con-ducted in partnership with the Sagkeeng First Nations community in Manitoba. The in-door environment of a school building is a complex system involving many parameters that may have an impact on indoor air quality and thermal comfort. Air quality in schools depends strongly, on one hand, on the interaction between the building and its outdoor environment, and, on the other hand, on the way a building is used, operated, and maintained. School buildings should use a properly designed heating, ventilation, and air conditioning system that maintains an adequate supply of cleaner air and sets up optimal heating. This study provided empirical evidence to the claim that First Nations schools can be exposed to adverse indoor environmental conditions based on the school studied in this research. The evidence was based on objective measurements of indoor environmental quality and a subjective survey conducted among the school’s teachers. In addition, a series of statistical analyses in Statistical Package for Social Science 25 and Excel were performed to examine the associations between building characteristics, indoor environmental parameters, and teacher's health. Finally, thermal imaging was conducted to check the thermal performance of the building envelope. The physical measurement campaign consisted of measuring the indoor environmental parameters: indoor air temperature, relative humidity, and carbon dioxide concentrations in 9 classrooms. Consequently, along with the comprehensive physical measurements, fundamental knowledge, of the building and its systems was also needed to identify principal factors that adversely affect indoor school environments. On-site continuous records of air temperature showed underheating and overheating of the school during the year. Air temperatures as high as 35.1°C (winter season) and as low as 9.6°C (spring season) were recorded in the school during occupied hours. Indoor air was dry in the winter as relative humidity values were less than 30%. Moreover, thermal comfort parameters were found to be influenced by the heating, ventilation, and air conditioning system, poor thermal performance of the building envelope, and classroom orientation. The results of the subjective survey demonstrated that approximately 70% of teachers reported poor indoor air quality as the biggest problem in the school. Statistically significant associations (p < 0.05) were found between teachers’ satisfaction with class-rooms’ indoor air quality and their characteristics such as ventilation system, maintenance, and cleanliness. The fifth part of the school pedagogical staff pointed out that poor indoor air quality was the main source of health problems such as headaches, fatigue, aggravation of asthma and allergies, stuffy nose, and difficulties with breathing. The relationships between physical measurements (i.e., temperature, relative humidity, and carbon dioxide) and the score of having adverse health symptoms were evaluated using the linear regression model. The decrease in mean indoor air temperature was statistically significantly associated with an increase in the score of having sick building syndrome (Beta= -1.163, CI [-2.104; -0.221], p < 0.05) while the increase in relative humidity was statistically significantly associated with an increase in the score of having sick building syndrome (Beta = 0.783, CI [1.561; 0.005], p < 0.05). Furthermore, the associations between performance and satisfaction with indoor air quality, acoustics, and lighting comfort were found to be statistically significant (p < 0.05).
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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) | 0,001 | 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 ».