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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.

In this study, we measured the size distribution of particles ranging in size from 5.6 to 560 nm that were emitted between brake disks and pads under various braking conditions to observe and analyze changes to the resulting particle size distribution over braking time. A peak of 178-275 nm (200 nm peak) was observed in all braking conditions. However, the generation of spherical particles of a 10 nm range was observed only when the disk speed and brake force were above certain levels and intensified only when speed and brake force further increased. The total number concentration of ultrafine particles (no larger than 0.1 μm; PM0.1) generated was found to correlate with disk speed and brake force. Thus, the generation of nanoparticles resulting from disk speed and brake force was attributable primarily to increases in the contact surface temperature. The critical temperature for the generation of nanoparticles of a 10 nm range was found to be about 70 °C, which is the average temperature between the surface and the inside of the disk. If the speed or brake force was higher, that is, the temperature of the contact surface reached a certain level, evaporation and condensation took place. Vapor then left the friction surface, met with the air, and quickly cooled to form nanoparticles through nucleation. When the newly generated particles became highly concentrated, they grew through coagulation to form agglomerates or the vapor condensed directly onto the surface of existing particles of about 200 nm (formed by mechanical friction).