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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.

Contributions of exhaust and non-exhaust sources to traffic-related particulate matter (PM) pollution in the atmosphere are almost identical and the most important non-exhaust source is known to be brake wear particles. In order to understand the properties and harmful effects of wear particles on people, accurate information on size distribution of brake wear particles is needed. Our previous study investigated the measured changes in size distribution of nanoparticles of 500 nm or smaller to understand the origin of nanoparticles due to temperature increases on the friction surface. The present study was intended to investigate the characteristics of size distribution (5.6 nm–32 μm) of PM released under different braking conditions by using different instruments. The measurement results under 9 braking conditions using 3 different instruments showed that the size distribution characteristics of particles can be divided into two main types according to braking energy. The first type is of PM up to 10 μm in size and with a peak number concentration at 0.2–0.75 μm regardless of braking energy, while the second type is of PM around 10 nm in size generated only when braking energy increased and particles that increased up to 100 nm. In addition, we found that the size distributions measured by the optical particle counter (OPC) and the aerodynamic particle sizer (APS) were consistent by assuming a mean diameter ratio of two instruments.

Exposure to airborne particulate matter (PM) causes several diseases in the human body. The smaller particles, which have relatively large surface areas, are actually more harmful to the human body since they can penetrate deeper parts of the lungs or become secondary pollutants by bonding with other atmospheric pollutants, such as nitrogen oxides. The purpose of this study is to present the number of PM inhaled by subway users as a possible reference material for any analysis of the hazards to the human body arising from the inhalation of such PM. Two transfer stations in Seoul, Korea, which have the greatest number of users, were selected for this study. For 0.3–0.422 μm PM, particle number concentration (PNC) was highest outdoors but decreased as the tester moved deeper underground. On the other hand, the PNC between 1 and 10 μm increased as the tester moved deeper underground and showed a high number concentration inside the subway train as well. An analysis of the particles to which subway users are actually exposed to (inhaled particle number), using particle concentration at each measurement location, the average inhalation rate of an adult, and the average stay time at each location, all showed that particles sized 0.01–0.422 μm are mostly inhaled from the outdoor air whereas particles sized 1–10 μm are inhaled as the passengers move deeper underground. Based on these findings, we expect that the inhaled particle number of subway users can be used as reference data for an evaluation of the hazards to health caused by PM inhalation.

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).