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Byline: Soon-Bark Kwon (Railroad Environment Research Department, Korea Railroad Research Institute, Korea, sbkwon@krri.re.kr); Youngmin Cho (Railroad Environment Research Department, Korea Railroad Research Institute, Korea); Duckshin Park (Railroad Environment Research Department, [...]

A hybrid dust collector attached to a subway train bottom was developed for the effective removal of subway particulate matter (PM), by considering the fluctuating speed of a subway train between stations. It combines an electrostatic precipitator, which has good collection efficiency when the flow velocity is low, and an inertial dust separator, which has good collection efficiency when the flow velocity is high. The electrostatic precipitator and inertial dust separator guarantee a high collection efficiency regardless of the subway train operating speed by compensating for the other's shortcomings. Wind tunnel test and numerical simulation were conducted to verify the performance of the hybrid dust collector. The experiment and simulation results were compared to verify the prediction accuracy of the simulation method. The collection efficiencies of the electrostatic precipitator and inertial dust separator were then simulated for various operating speeds of the subway train to predict the overall collection efficiency of the hybrid dust collector. As a result, when airflow velocity at the hybrid dust collector inlet varied from 2 to 8 m/s, subway PM10 or PM2.5 collection rate of a single hybrid dust collector was predicted to be in the range 88 ∼ 123 μg/s or 30 ∼ 35 μg/s, respectively. The use of multiple such hybrid dust collectors attached to the subway train bottom is expected to be effective in reducing fine dust concentration in subway tunnels. Copyright © 2019 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Increasing concerns about the spread of airborne pathogens such as severe acute respiratory syndrome (SARS) and novel swine-origin influenza A (H1N1) have attracted public attention to bioaerosols and protection against them. The airborne pathogens are likely to be expelled from coughing or speaking, so the physical data of the exhaled particles plays a key role in analyzing the pathway of airborne viruses. The objective of this study was to analyze the initial velocity and the angle of the exhaled airflow from coughing and speaking of 17 males and 9 females using Particle Image Velocimetry (PIV) and acrylic indoor chamber. The results showed that the average initial coughing velocity was 15.3m/s for the males and 10.6m/s for the females, while the average initial speaking velocity was 4.07m/s and 2.31m/s respectively. The angle of the exhaled air from coughing was around 38° for the males and 32° for the females, while that of the exhaled air from speaking was around 49° and 78° respectively. Also, the linear relation between the tested subject’s height and their coughing and speaking velocity was shown in this study. [Copyright &y& Elsevier]

Abstract: Numerous reports have shown that fine particulates threaten human health. Since their health impact is associated with both mass and number concentrations, it is necessary to evaluate the emission standards for particulate mass accordingly. This study examined the particulate matter characteristics of diesel locomotive engine exhaust at various engine ratings. Diesel engine exhaust was collected via a dilution tunnel and the concentration and size distribution of fine particles were measured by a scanning mobility particle sizer. Exhaust gasses were measured simultaneously by a stack sampler. The maximum carbon monoxide emission was reached at 59% of the maximum rating, after which emissions decreased. The particle count median diameter increased with the engine rating, until a maximum was reached at 40% of the maximum rating. Most exhaust particles were nanoparticles with the nuclei mode range, a particle diameter (DP)<50nm. The increase in particles with the accumulation mode range, 50

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]

Emission reduction is one of the most efficient control measures in fuel-powered locomotives. The purpose of this study was to determine the reduction in particulate matter (PM) and black carbon (BC) emissions following the installation of a fuel activation device (FAD). The FAD was developed to enhance fuel combustion by atomizing fuel and to increase the surface area per unit volume of injected fuel. Emission reduction by the FAD was evaluated by installing a FAD in an operating diesel locomotive in Mongolia. The test was conducted on a train operating on a round-trip 238-km route between Ulaanbaatar and Choir stations in Mongolia. The fuel consumption rate was slightly reduced following the FAD installation. The FAD installation decreased PM and BC emissions in the diesel locomotive, especially coarse PM. The PM 10 reductions achieved after FAD installation were 58.0, 69.7, and 34.2% for the constant velocity, stopping, and acceleration stages of the train's operation, respectively. The BC reduction rates were 29.5, 52.8, and 27.4% for the constant velocity, stopping, and acceleration stages, respectively. [ABSTRACT FROM AUTHOR]

Abstract: As devices become smaller to the extent of nano-scale, dry damage-free cleaning process has become an important area of study. Gas cluster cleaning technology has a potential to remove contaminant particles from the surface of semiconductor wafers without damage because a gas cluster is smaller than 100nm in diameter. In this paper, a new dry cleaning process using a CO2 gas cluster beam is developed and a characterization method for gas cluster generation was studied experimentally. First, a CO2 gas cluster cleaning system was built to evaluate the feasibility of cleaning by use of a gas cluster beam, which is generated by expansion of gas through a converging–diverging nozzle and nozzle chilling. Cleaning tests were performed several times. Twenty-five to three hundred nanometers silica particles were cleaned off from the surface of a bare Si wafer and of a wafer with 60nm poly-silicon structure by using this cleaning system. Preliminary result showed that the contaminants were successfully removed without damage to the wafer surface. To characterize the gas cluster, we used the particle beam mass spectrometer (PBMS) for the measurement of the size distribution of the gas clusters. The size distribution of clusters is measured by varying flow rate and nozzle coolant temperature. From the measurement results, the size of a gas cluster was less than 40nm, which is expected to cause no damage on a surface based on the measurement of pattern collapse force. By using the PBMS, CO2 gas cluster cleaning system can be optimized to provide the maximum cleaning efficiency for nano-scale contaminant particles without damage to substrate surfaces. [Copyright &y& Elsevier]