• Proceedings of the Korea Air Pollution Research Association Conference
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• 2005.11a
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• pp.329-331
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• 2005

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2019.11a
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• pp.85-86
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• 2019

• Plant Journal
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• v.18 no.1
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• pp.50-54
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• 2022

The demand for high-efficiency dust collectors is rapidly increasing to remove PM from exhaust gas emission facilities, such as thermal power plants, steel mills, and industrial cogeneration plants, as the Pmemission standards have been strengthened. In this study, the electrospray is adapted for existing electrosratic precipitator(EP) to remedy its shortcomings and to improve the performance. Electrospray has been mainly used for the purpose of generating very fine droplets, but fir the purpose of EP, the flow rate over 10 mL/min per nozzleis required, and a high flow rate condition of 65 to 200 times is required. The electrospray of high flow rate has a completely different spray shape from the low flow rate condition, and was visualized through various figures such as corona discharge photographs and shadow images.

• Economic and Environmental Geology
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• v.54 no.4
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• pp.475-482
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• 2021

This study proposed a strategy with literature review on effective monitoring of dispersion of the particulate matters (PM) emitted from domestic open pit lime mines. The mines generally produced a large amount of PM through the mine processes such as crushing and transportation of raw or crushed ores. The PM emission from mine facilities or transportation can be assessed using empirical equations which was prepared through the experimental tests to produce PM from ores. For effective monitoring of mine PM dispersion, this study showed a preliminary application of the monitoring network with multiple low-cost sensors around a main PM emission source for each mine site. Therefore, two domestic limestone mine sites were selected for this study, and install the monitoring network with low-cost PM sensors and LTE (Long-term evolution) data communication. Then, preliminary resultant PM data plotted according to monitoring duration showed typical PM dispersion patterns. The quantification of the PM dispersion patterns should be roughly prepared by a PM size-dependent dispersion modeling.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.115-121
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• 2017

The efficiency of railway track cleaning vehicle for eliminating fine particulate matter (PM10 and PM2.5) in a subway tunnel depends strongly on the structure of the air blowing and suction system installed under the train. To increase the efficiency of underbody suction system, this paper proposes a novel method to use the Coanda effect for the air blower and dust suction module. In particular, through Computational Fluid Dynamics (CFD) analysis, the flow control device induced by the Coanda effect enables an increase in the overall flow velocity and to stabilize the flow distribution of the suction module at a control angle of $90^{\circ}$. In addition, the flow velocity drop at the edge of the air knife-type blower can be improved by placing small inserts at the edge of the blower. Those 4 modular designs of the dust suction system can help remove the dust accumulated on the track and tunnel by optimizing the blowing and suction flows.

• Journal of Applied Biological Chemistry
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• v.63 no.2
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• pp.161-168
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• 2020

It is known that different plant species have ability to deposit different amounts of particulate matter (PM) on their leaves and plants can absorb heavy metals in PM through their leaves. Heavy metals in PM can have toxic effect on human body and plants. Therefore, PM on different roadside trees at Chungbuk national University including box tree (Buxus koreana), yew (Taxus cuspidate), royal azalea (Rhododendron yedoense), and retusa fringetree (Chionanthus retusa) was quantified based on particle size (PM_>10 and PM_2.5-10). The metal concentration in PM accumulated on leaves was analyzed using inductively coupled plasma-mass spectroscopy. In this study, the mass of PM_>10 deposited on the surface of the tree leaves ranged from 6.11 to 32.7 ㎍/㎠, while the mass of PM_2.5-10 ranged from 0 to 14.8 ㎍/㎠. The royal azaleas with grooves and hair on the leaf surface retained PM particles for longer time, while the yews and box trees with wax on leaf surfaces accumulated more PM. The PM contained elements in crustal material such as Al, Ca, Mg, and Fe and heavy metals including Cu, Pb and Zn. The concentration of elements in crustal material was higher in the coarser size, while heavy metal concentration was relatively higher in the finer size fraction. The Mn, Cd, Cu, Ni, Pb, and Zn concentrations of leaves and PM_2.5-10 were significantly correlated indicating that PM was taken up through tree leaves.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.290-297
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• 2016

The performance of track cleaning trains to remove accumulated fine particulate matter in subway tunnels depends on the design of the suction system equipped under the train. To increase the efficiency of the suction system under the cleaning vehicle, this paper proposes a novel dust suction module equipped with both air blowing nozzles and a dust suction structure. Computational Fluid Dynamics (CFD) analysis with turbulent flow was conducted to optimize the dust suction system with a particle intake and blowing function. The optimal angle of the air blowing nozzle to maximize the dust removal rate was found to be 6 degrees. The performance of the track cleaning vehicle can be increased by at least 10 percent under an operation speed of 5km/h.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.57-70
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• 2023

In recent years, the frequency of warnings about particulate matter (PM) has gradually increased in Korea, along with an increase in its intensity. Because of their vast surface area, reactivity to external particles, and characteristics of their leaves, urban trees can act as biofilters, reducing PM pollution. However, the air pollutant PM can cause various types of damage not only to human health but also to vegetation. Studies performed to date on the responses of trees to PM are still insufficient. Here, we analyzed the correlation between PM adsorption and physiological and biochemical responses of four major street tree species, namely, Abies holophylla, Acer buergerianum, Pinus densiflora, and Quercus variabilis, under conditions of approximately 300 ㎍ m^-3 of fly ash emissions using a phytotron. The results showed that the physiological and biochemical responses and PM adsorption differed depending on the tree species. In correlation analysis, it was confirmed that there were positive correlations between physiological factors, and PM adsorption on adaxial leaf surfaces negatively impacted the physiological characteristics. This study provides fundamental information for selecting tree species to reduce PM pollution and develop sustainable urban forests.