Search

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN’s performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67 ∼ 80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. [ABSTRACT FROM AUTHOR]

Given that around eight million commuters use the Seoul Metropolitan Subway (SMS) each day, the indoor air quality (IAQ) of its stations has attracted much public attention. We have monitored the concentration of particulate matters (PM x ) (i.e., PM 10 , PM 2.5 , and PM 1 ) in six major transfer stations per minute for three weeks during the summer, autumn, and winter in 2014 and 2015. The data were analyzed to investigate the relationship between PM x concentration and multivariate environmental factors using statistical methods. The average PM concentration observed was approximately two or three times higher than outdoor PM 10 concentration, showing similar temporal patterns at concourses and platforms. This implies that outdoor PM 10 is the most significant factor in controlling indoor PM concentration. In addition, the station depth and number of trains passing through stations were found to be additional influences on PM x . Principal component analysis (PCA) and self-organizing map (SOM) were employed, through which we found that the number of trains influences PM concentration in the vicinity of platforms only, and PM x hotspots were determined. This study identifies the external and internal factors affecting PM x characteristics in six SMS stations, which can assist in the development of effective IAQ management plans to improve public health. [ABSTRACT FROM AUTHOR]