Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models

Usually, various types of hazardous pollutants remain accumulated in underground subway stations of metro system. To control indoor air quality (IAQ) in subway stations, the control strategies based on the predictive model which does not have the effect of temperature due to seasonal variations, have been currently used. In this paper, season dependent models for monitoring and prediction of IAQ, which take care of seasonal changes, are proposed. The real time data of various pollutants (namely, concentration of PM10 and PM2.5 on platform, temperature, humidity and the concentration of nitrogen) during March 2008 to February 2009 are obtained from Seoul subway station. MANOVA test has been carried out to know the quantitative measure of the differences among different data sets of three seasons (spring and fall, summer, and winter). PCA and PLS regression methods are applied on data sets of one year (to develop global model) and four seasons (to develop seasonal models) to monitor and predict the IAQ. The results of this study show that the seasonal models can predict the future data of PM10 and PM2.5 precisely than the global model.