Jo-Chun Kim

Chemical speciation of size-segregated floor dusts and airborne magnetic particles collected at underground subway stations in Seoul, Korea

Previous studies have reported the major chemical species of underground subway particles to be Fe-containing species that are generated from wear and friction processes at rail-wheel-brake and catenaries-pantographs interfaces. To examine chemical composition of Fe-containing particles in more details, floor dusts were collected at five sampling locations of an underground subway station. Size-segregated floor dusts were separated into magnetic and non-magnetic fractions using a permanent magnet. Using X-ray diffraction (XRD) and scanning electron microscopy/energy dispersive X-ray spectrometry (SEM/EDX), iron metal, which is relatively harmless, was found to be the dominating chemical species in the floor dusts of the <25 μm size fractions with minor fractions of Mg, Al, Si, Ca, S, and C. From SEM analysis, the floor dusts of the <25 μm size fractions collected on railroad ties appeared to be smaller than 10 μm, indicating that their characteristics should somewhat reflect the characteristics of airborne particles in the tunnel and the platform. As most floor dusts are magnetic, PM levels at underground subway stations can be controlled by removing magnetic indoor particles using magnets. In addition, airborne subway particles, most of which were smaller than 10 μm, were collected using permanent magnets at two underground subway stations, namely Jegi and Yangjae stations, in Seoul, Korea. XRD and SEM/EDX analyses showed that most of the magnetic aerosol particles collected at Jegi station was iron metal, whereas those at Yangjae station contained a small amount of Fe mixed with Na, Mg, Al, Si, S, Ca, and C. The difference in composition of the Fe-containing particles between the two subway stations was attributed to the different ballast tracks used.

Seasonal variations of monoterpene emissions from Pinus densiflora in East Asia.

The emission rates and compositions of monoterpene from Pinus densiflora were investigated in the Gumsung (GM) and Worak (WM) mountains. The standard emission rates (ERs: ERs is the monoterpene emission rate at standard temperature, 30 degrees C) from P. densiflora ranged from 0.817 to 1.704 (microgC/gdw-h). The ERs and beta-values of total monoterpene were measured at the two study sites (GM and WM). In the spring and summer, the ERs were the highest, while relatively low values (<0.058microgC/gdw-h) were measured in the autumn and winter. In GM and WM sites the beta-value obtained for the different seasons ranged from 0.047 to 0.179, with an average of 0.09. The major monoterpene compounds from P. densiflora were alpha-pinene, myrcene, beta-phellandrene, d-limonene and alpha-terpinene. The fractional compositions of individual monoterpene compounds were significantly different between the two test sites in the summer and winter. The ERs of the older group (31-40 years) were higher than those in the younger group (21-30 years). However, the monoterpene compositions were similar between the two age groups.

Iron Speciation of Airborne Subway Particles by the Combined Use of Energy Dispersive Electron Probe X-ray Microanalysis and Raman Microspectrometry

Quantitative energy-dispersive electron probe X-ray microanalysis (ED-EPMA), known as low-Z particle EPMA, and Raman microspectrometry (RMS) were applied in combination for an analysis of the iron species in airborne PM10 particles collected in underground subway tunnels. Iron species have been reported to be a major chemical species in underground subway particles generated mainly from mechanical wear and friction processes. In particular, iron-containing particles in subway tunnels are expected to be generated with minimal outdoor influence on the particle composition. Because iron-containing particles have different toxicity and magnetic properties depending on their oxidation states, it is important to determine the iron species of underground subway particles in the context of both indoor public health and control measures. A recently developed analytical methodology, i.e., the combined use of low-Z particle EPMA and RMS, was used to identify the chemical species of the same individual subway particles on a single particle basis, and the bulk iron compositions of airborne subway particles were also analyzed by X-ray diffraction. The majority of airborne subway particles collected in the underground tunnels were found to be magnetite, hematite, and iron metal. All the particles collected in the tunnels of underground subway stations were attracted to permanent magnets due mainly to the almost ubiquitous ferrimagnetic magnetite, indicating that airborne subway particles can be removed using magnets as a control measure.

Sensor Validation for Monitoring Indoor Air Quality in a Subway Station

Indoor air quality (IAQ) is an important factor, which can influence the health and comfort of passengers in subway stations. Various types of hazardous pollutants, such as particulate matters, remain accumulated in the subway space due to overcrowding and inadequate ventilation system. Subway stations are extremely crowded during rush hours and indoor air of the subway stations could be strongly affected, which in turn, affects passengers’ respiratory system. In this study, several key air pollutants data were collected every minute by the air sampler and tele-monitoring system (TMS) to effectively monitor and control IAQ in subway stations. The quality of the online measurement could decide the failure and success in environmental process assessment. Therefore, prompt detection of the occurrence of sensor faults and identification of those locations are of primary importance for efficient monitoring and control of IAQ. In this paper, Principal components analysis based approach is used to detect, identify and reconstruct the sensor faults in monitoring IAQ. Four types of sensor failures, namely, bias, drifting, complete failure and precision degradation are tested for monitoring IAQ. Several test results of a real subway TMS showed that the developed sensor validation technique can work well for the four kinds of sensor faults.

Review of canine rabies prevalence under two different vaccination programmes in Korea

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Electron Beam Irradiation for Mercury Oxidation and Mercury Emissions Control

A variety of air pollution control strategies have been investigated to reduce mercury emissions from coal-fired sources. The most developed and deployed technologies are based on adsorption of mercury onto powdered activated carbon followed by carbon collection. Mercury oxidation over selective catalytic reduction catalysts followed by wet scrubbing is another potential technique, and tests suggest that emissions reductions of 20–80% are possible, but test results are variable and ultrahigh removal ( 95%+ ) is unusual. The objective of this study was to investigate the effectiveness of electron beam irradiation to oxidize mercury vapor, to improve mercury removal with wet scrubbers or wet electrostatic precipitators (ESPs). Metallic mercury vapor samples in air and other atmospheres were prepared at concentrations of approximately 16 μ g/m3 . Samples were electron irradiated at energy levels of 2.5–10 kGy, equivalent to 3.1–12.4 kJ/ m3 stack gas. Results show that mercury oxidation rate was dependent on ...

Novel hybrid technology for VOC control using an electron beam and catalyst

Noble (Pt, Pd) and transition metals (Mn, Cu) were employed as coupling catalysts to evaluate the toluene (1500 ppm C of initial concentration) removal efficiencies in the electron beam (EB)-catalyst coupling system. The toluene removal efficiency was 60.1% in the EB-only system at a dose of 8.7 kGy. In the presence of the metal catalysts (Pt, Pd, Cu and Mn), the removal efficiency was enhanced by 37, 33, 6 and 22%, respectively, compared to that of EB-only treatment. It was found that the selectivity to CO2 with Pt and Pd coupling were relatively higher than those of Cu and Mn. Especially the CO2 selectivity of EB-Pt coupling was significantly high at a relatively low absorbed dose. The removal efficiencies were compared for loading of catalyst and there was no significant difference among 0.1, 0.5 and 1.0 wt%.

Removal of particulate matter emitted from a subway tunnel using magnetic filters.

We removed particulate matter (PM) emitted from a subway tunnel using magnetic filters. A magnetic filter system was installed on the top of a ventilation opening. Magnetic field density was increased by increasing the number of permanent magnet layers to determine PM removal characteristics. Moreover, the fans frequency was adjusted from 30 to 60 Hz to investigate the effect of wind velocity on PM removal efficiency. As a result, PM removal efficiency increased as the number of magnetic filters or fan frequency increased. We obtained maximum removal efficiency of PM10 (52%), PM2.5 (46%), and PM1 (38%) at a 60 Hz fan frequency using double magnetic filters. We also found that the stability of the PM removal efficiency by the double filter (RSD, 3.2-5.8%) was higher than that by a single filter (10.9-24.5%) at all fan operating conditions.

Installation of platform screen doors and their impact on indoor air quality: Seoul subway trains

In this study, variations of particulate matter (PM) concentrations in subway trains following installation of platform screen doors (PSDs) in the Seoul subway system were investigated. PM samples were collected in the trains on subway lines 1–8 before and after installation of PSDs. It was found that the mean PM10 concentration in the trains after PSDs installation increased significantly by 29.9% compared to that before installation. In particular, the increase of PM10 in line 6 was the highest at 103%. When the relationship between PM10 and PM2.5 was compared, coefficients of determination (r2) before and after PSDs installations were 0.696 and 0.169, respectively. This suggests that air mixing between the platform and the tunnel after PSDs installation was extremely restricted. In addition, the indoor/outdoor PM10 ratio following PSDs installation increased from 1.32 to 2.97 relative to the period with no installed PSDs. Furthermore, this study revealed that PM levels in subway trains increased significantly after all underground PSDs were put in use. Several potential factors were examined that could result in this PM increase, such as train ventilation systems, operational conditions, passenger volume, subway depth, and the length of underground segments. Implications PM10 concentrations inside the subway trains increased after PSDs installation. This indicates that air quality in trains was very seriously impacted by PSDs. PM10 levels were also influenced by the tunnel depth and length of the underground segments. To prevent the adverse effect on human health by PM10 emitted from the tunnel, an applicable ventilation system to reduce PM10 is required inside trains and tunnels.

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM10, CO2, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher’s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.