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 (CO2
) 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 CO2
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 CO2
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 CO2
level): Air change rate (ACR) > room operations (occupant number and light system) > outdoor conditions.
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