Indoor air quality (IAQ) is one of the major concerns to people who spend most of the time in an indoor environment, as many hazardous pollutants are emitted from the buildings or underground spaces, or enter from outside sources through the various vents and other entry/exit points. In particular, the IAQ in subway stations have periodic variations in indoor air pollutants, depending on the numbers of passengers and trains, as well as the previous IAQ. Global and weekly models (divided into weekday and weekend model) have been developed and compared with one another using parallel factor analysis to consider the periodic characteristics, as IAQ shows changeable aspects within a day or the week, owing to differences in the passengers’ travel patterns. The IAQ monitoring results of the weekly model showed that the proposed monitoring method could detect abnormal IAQ conditions in a timely manner when compared to the global model. The proposed monitoring method could identify not only the most active time periods but also the main contaminant sources resulting in the abnormality of the IAQ, based on fault detection.
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