Indoor Environmental Quality Evaluation of Lecture Classrooms in an Institutional Building in a Cold Climate

Lexuan Zhong, Jing Yuan and Brian Fleck
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Lexuan Zhong: Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Jing Yuan: Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Brian Fleck: Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Sustainability, 2019, vol. 11, issue 23, 1-16

Abstract: In this paper, ventilation, indoor air quality (IAQ), thermal and acoustic conditions, and lighting were studied to evaluate the indoor environmental quality (IEQ) in an institutional building at the University of Alberta in Edmonton, Canada. This study examined IEQ parameters, including pressure, illuminance, acoustics, carbon dioxide (CO 2 ) concentration, temperature, and humidity, with appropriate monitors allocated during a lecture (duration 50 min or 80 min) in four lecture classrooms repeatedly ( N = 99) from October 2018 to March 2019 with the objectives of providing a comprehensive analysis of interactions between IEQ parameters. The classroom environments were maintained at 23 ± 1 °C and 33% ± 3% RH during two-season measurements. Indoor mean CO 2 concentrations were 550–1055 ppm, and a mean sound level of 58 ± 3 dBA was observed. The air change rates were configured at 1.3–6.5 per hour based on continuous CO 2 measurements and occupant loads in the lectures. A variance analysis indicated that the within-lecture classroom variations in most IEQ parameters exceeded between-lecture classrooms. A multilayer artificial neural network (ANN) model was developed on the basis of feedforward networks with a backpropagation algorithm. ANN results demonstrated the importance of the sequence of covariates on indoor conditions (temperature, RH, and CO 2 level): Air change rate (ACR) > room operations (occupant number and light system) > outdoor conditions.

Keywords: indoor environmental quality (IEQ); lecture classrooms; HVAC systems; ventilation; correlation; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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