Longitudinal Assessment of Thermal and Perceived Air Quality Acceptability in Relation to Temperature, Humidity, and CO2 Exposure in Singapore

Published In

Building and Environment

Document Type


Publication Date



Thermal acceptability (TA) and perceived air quality acceptability (PAQA) are typically analyzed in climate chambers or cross-sectional field studies. Individual factors, such as expectations and perceived environment history, may influence the acceptability response. Longitudinal studies with multi-day design are absent in the literature. Fifteen Singaporean subjects participated in a 7-day longitudinal experiment in which they carried a portable sensor that continuously recorded personal air temperature, relative humidity and carbon dioxide concentration at 1-min intervals. Instantaneous TA and PAQA were regularly sampled by survey for each subject. High acceptability was found at home, restaurants and workplaces, whereas low acceptability was found for outdoor and transport environments. The participants, from Singapore's modern tropical environment spent an average of 96% of their time indoors. Weak associations were reported between acceptabilities and measured physical parameters taken independently. Clustering data by location, subject's sleeping ventilation habit, air-conditioning operation status and the changes in physical parameters over a designated time period enhanced the understanding of the acceptability results. In general, acceptability was lower for those who slept in air-conditioned environments than for those who slept without air-conditioning. The carbon dioxide mixing ratio was critical for PAQA predictions but not for TA. The Gaussian process (GP) had a better predictive power than a multiple linear regression approach. Using GP, we found that a general predictive model had comparable simulation performance as for individual predictive models. The longitudinal experiment has demonstrated effectiveness for TA and PAQA analysis, which could be beneficial to future studies in personal comfort prediction.


additional authors