Modeling ventilation rates in bedrooms based on building characteristics and occupant behavior

• Published in: Building and environment 2011-11, Vol.46 (11), p.2230-2237
• Main Authors: Bekö, Gabriel, Toftum, Jørn, Clausen, Geo
• Format: Article
• Language: English
• Subjects: Applied sciences; Bedrooms; Building characteristics; Building technical equipments; Buildings; Buildings. Public works; Carbon dioxide; Computation methods. Tables. Charts; Condensing; Environmental engineering; Exact sciences and technology; Grounds; Homes; Inspection; Linear regression model; Mathematical models; Occupant behavior; Parents; Regression; Residential building; Structural analysis. Stresses; Types of buildings; Ventilation; Ventilation rate; Ventilation. Air conditioning
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2011.05.002

Air change rate (ACR) data obtained from the bedrooms of 500 Danish children and presented in an earlier paper were analyzed in more detail. Questionnaires distributed to the families, home inspections and interviews with the parents provided information about a broad range of residential characteristics and occupant behavior. These were tested in several linear regression models to identify the degree of effect each selected independent variable has on the total ACR. The measured ACRs are summarized by some of the most significant variables such as room volume (higher ACR in smaller rooms), number of people sleeping in the bedroom (higher ACR with more people), average window and door opening habits (higher ACR with more opening), sharing the bedroom with other family members (higher ACR in shared rooms), location of the measured room (higher ACR above ground floor), year of construction (lowest ACR in buildings from early 1970s), observed condensation on the bedroom window (higher ACR at less condensation), etc. The best-fitting model explained 46% of the variability in the air change rates. Variables related to occupant behavior were stronger predictors of ventilation rate (model R 2 = 0.30) than those related to building characteristics (model R 2 = 0.09). Although not perfectly accurate on a room-to-room basis, our best-fitting model may be useful when a rough estimate of the average air change rate for larger study populations is required in future indoor air quality models.