• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.55-62
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• 2008

This study is aimed at substantially reducing the particulate matter (dust) emission during the combustion of heavy fuel in boilers by addition of combustion improver. The combustion improver used were the oil-soluble organometallic compounds that were found to be more effective than the dispersing agents that are generally used for reducing the particulate emission. The dust reduction effect was found to depend on the active materials (metals) as well as on the organic ligand part of organometallic compounds. Acetylacetonoate and naphthenate of Fe and Ca were found to be most effective for dust reduction. Addition of Fe and Ca organometallic compounds as combustion improver in concentration of 30 ppm (metal basis) to heavy fuel oil, caused dust reduction by 50 wt% to 80 wt%.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2107-2115
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• 2018

Over recent years, high-concentration of particulate matters (e.g., a.k.a. fine dust) in South Korea has increasingly evoked considerable concerns about public health. It is intractable to track and report $PM_{10}$ measurements to the public on a real-time basis. Even worse, such records merely amount to averaged particulate concentration at particular regions. Under this circumstance, people are prone to being at risk at rapidly dispersing air pollution. To address this challenge, we attempt to build a predictive model via deep learning to the concentration of particulates ($PM_{10}$). The proposed method learns a binary decision rule on the basis of video sequences to predict whether the level of particulates ($PM_{10}$) in real time is harmful (>$80{\mu}g/m^3$) or not. To our best knowledge, no vision-based $PM_{10}$ measurement method has been proposed in atmosphere research. In experimental studies, the proposed model is found to outperform other existing algorithms in virtue of convolutional deep learning networks. In this regard, we suppose this vision based-predictive model has lucrative potentials to handle with upcoming challenges related to particulate measurement.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.89-96
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• 2020

Adverse impact of Particulate Matters(PM10, PM2.5; PMs) significantly affects daily lives. Major countermeasures for reducing concentration of PMs were focused on emission source without considering spatial difference of PMs concentration. Thus, this study analyzed spatial·temporal distribution of PMs with observation data as well as potential contributing factors on PMs concentration. The annual average concentration of PMs have been decreased while the particulate matter warnings and alerts were significantly increased in 2018. The average concentration of PMs in spring and winter was higher than the other seasons. Also, the spatial distribution of PMs were also showed seasonality while concentration of PMs were higher in Seoul-metropolitan areas in all seasons. Climate variables, emission source, spatial structure and potential PM sinks were selected major factors which could affects on ambient concentrations of PMs. This paper suggest that countermeasures for mitigating PM concentration should consider characteristics of area. Climatic variables(temperature, pressure, wind speed etc.) affects concentrations of PMs. The effects of spatial structure of cities(terrain, ventilation corridor) and biological sinks(green infrastructure, urban forests) on concentration of PMs should be analyzed in further studies. Also, seasonality of PMs concentration should be considered for establishing effective countermeasures to reduce ambient PMs concentration.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1465-1474
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• 2001

The dispersion stability of particles and the adsorption of surfactant were examined as a fundamental environment to adhesion of particulate soil to fabric. The adsorption of surfactant on the PEF fabrics decreased with the addition of electrolytes and decreased with increasing the ionic strengths showed similar tendency to PET fabric. And the dispersion force of $\alpha$-Fe$_2$O$_3$particles decreased with the addition of electrolyte. The adhesion of particulate soil to fabric, increased with decreasing the adsorption of surfactant the correlation between the two was high at low ionic strength. The correlation between the adsorption of surfactants on $\alpha$-Fe$_2$O$_3$ particles and the adhesion of particles to fabric was smaller as shown in the correlation between the adsorption of surfactants on fiber substrate and the adhesion of particles on fabric. However, the correlation between the adhesion of particles to fabric and the stability of particle dispersion was relatively more significant.

• International Journal of Automotive Technology
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• v.8 no.2
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• pp.165-177
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• 2007

Predictions of diesel particulate filtration are typically made by modeling of a particle collection, and providing particle trapping levels in terms of a pressure drop. In the present study, a series of single channel diesel particulate filter (DPF) experiments are conducted, the pressure traces are inversely analyzed and essential filtration parameters are deducted for model closure. A DPF filtration model is formulated with a non-linear description of soot cake regression. Dependence of soot cake porosity, packing density, permeability, and soot density in filter walls on convective-diffusive particle transportation is examined. Sensitivity analysis was conducted on model parameters, relevant to the mode of transition. Soot cake porosity and soot packing density show low degrees of dispersion with respect to the Peclet number and have asymptotes at 0.97 and $70\;kg/m^3$, respectively, at high Peclet number. Soot density in the filter wall, which is inversely proportional to filter wall Peclet number, controls the filtration mode transition but exerts no influence on termination pressure drop. The percolation constant greatly alters the extent of pressure drop, but is insensitive to volumetric flow rate or temperature of exhaust gas at fixed operation mode.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.167-173
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• 2005

Thermal regeneration means burning-off and cleaning-up the particulate matters piled up in DPF(diesel particulate filter), and it requires both high temperature $(550\~600^{\circ}C)$ and appropriate concentration of oxygen at DPF entrance. However, it is not easy to satisfy such conditions because of the low temperature window of the HSDI(high speed direct injection) diesel engine(approximately $200\~350^{\circ}C$ at cycle). Therefore, this study is focused on the method to raise temperature using the trade-off relation between temperature, oxygen concentration, and the influence of many parameters of common rail injection system including post injection. After performing an optimal mapping of the common rail parameters for regeneration mode, the actual cleaning process during regeneration mode is investigated and evaluated the availability of the regeneration mode mapping through regenerating soot trapped in the DPF.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.71-79
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• 2002

This research presents particulate distribution map of tidal flats of Hampyung bay using reflectance which extracted from satellite data and field survey data during same periods. The spectrum of particulate composition obtained from Landsat TM data was analysed and 7 scenes of satellite image were classified with ISODATA and K-MEANS methods. The results of unsupervised classification were estimated with in-situ data. The classification accuracy of ISODATA and K-MAMS methods were 84.3% and 85.7%. For validation of classified results of multi-temporal satellite images, TM image of May 1999(reference data), which was classified with field survey data was compared with classified results of multi-temporary satellite data.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.1
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• pp.74-80
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• 2019

Nonylphenol is an endocrine-disrupting chemical that is the degradation product of the nonionic surfactants nonylphenol ethoxylates. To understand the contamination and behavioral characteristics of nonylphenol, we measured the nonylphenol concentrations in surface water in the lower reaches of the Suyeong River, Korea. The results were used to estimate the material balance. The target area was divided into three regions to estimate the material balance of nonylphenol. In region 1, in flux of the dissolved nonylphenol was 282.3 g/day and the nonylphenol influx in particulate suspended solids was 1,582.8 g/day. The dissolved nonylphenol outflow discharged toward region 2 was 192.5 g/day, while the adsorption to particulate suspended solids was 89.8 g/day. Within the particulate suspended solids, the outflow to region 2 was 1,250.0 g/day, while the estimated amount settling in the sediments was 422.7 g/day. The adsorption of dissolved nonylphenol to the particulate suspended solids in regions 1 and 2 was 31.8% and 54.9%, respectively. In region 3, the desorption rate was 8.8%.

• Microbiology and Biotechnology Letters
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• v.49 no.3
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• pp.425-431
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• 2021

The use of big data may facilitate the recognition and interpretation of causal relationships between disease occurrence and climatic variables. Considering the immense contribution of rhinoviruses in causing respiratory infections, in this study, we examined the effects of various climatic variables on the seasonal epidemiology of rhinovirus infections in the temperate climate of Cheonan, Korea. Trends in rhinovirus detection were analyzed based on 9,010 tests performed between January 1, 2012, and December 31, 2018, at Dankook University Hospital, Cheonan, Korea. Seasonal patterns of rhinovirus detection frequency were compared with the local climatic variables for the same period. Rhinovirus infection was the highest in children under 10 years of age, and climatic variables influenced the infection rate. Temperature, wind chill temperature, humidity, and particulate matter significantly affected rhinovirus detection. Temperature and wind chill temperature were higher on days on which rhinovirus infection was detected than on which it was not. Conversely, particulate matter was lower on days on which rhinovirus was detected. Atmospheric pressure and particulate matter showed a negative relationship with rhinovirus detection, whereas temperature, wind chill temperature, and humidity showed a positive relationship. Rhinovirus infection was significantly related to climatic factors such as temperature, wind chill temperature, atmospheric pressure, humidity, and particulate matter. To the best of our knowledge, this is the first study to find a relationship between daily temperatures/wind chill temperatures and rhinovirus infection over an extended period.

• Food Science and Industry
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• v.54 no.1
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• pp.29-33
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• 2021

Particulate matter (PM) is an air pollutant that causes serious environmental problems in Korea and other countries. The annual average PM10 concentration in Korea is around 40 ㎛/㎥, which is more than twice as high as the WHO recommended standard. When consumed with food, fine PM can pose a risk to humans. However, the risk of fine PM has been focused on the risk of fine PM introduced through the respiratory system. We investigated the quantitative measuring methods of PM10 on food surface to identify possible risk analysis of fine PM. The surfaces of food with artificially contaminated PM10 were observed with a scanning electron microscope(SEM). An automatic object-based image analysis was used to analyze the amount and size distribution of particulate matter contained in SEM micrographs.