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Indoor air quality (IAQ) is an important factor, which can influence the health and comfort of passengers in subway stations. Various types of hazardous pollutants, such as particulate matters, remain accumulated in the subway space due to overcrowding and inadequate ventilation system. Subway stations are extremely crowded during rush hours and indoor air of the subway stations could be strongly affected, which in turn, affects passengers’ respiratory system. In this study, several key air pollutants data were collected every minute by the air sampler and tele-monitoring system (TMS) to effectively monitor and control IAQ in subway stations. The quality of the online measurement could decide the failure and success in environmental process assessment. Therefore, prompt detection of the occurrence of sensor faults and identification of those locations are of primary importance for efficient monitoring and control of IAQ. In this paper, Principal components analysis based approach is used to detect, identify and reconstruct the sensor faults in monitoring IAQ. Four types of sensor failures, namely, bias, drifting, complete failure and precision degradation are tested for monitoring IAQ. Several test results of a real subway TMS showed that the developed sensor validation technique can work well for the four kinds of sensor faults.

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Nomenclature SPE ¼ squared prediction error PMs ¼ particulate matters SSR ¼ sum square of residuals F ¼ number of factors extracted in each mode Q ¼ square prediction error IAQ ¼ indoor air quality KDE ¼ kernel density estimation MOE Ministry of Environment PARAFAC ¼ parallel factor analysis ß The Author(s), 2012. Reprints and permissions: http://www.sagepub.co.uk/journalsPermissions.nav