• Particle and aerosol research
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• v.16 no.1
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• pp.9-17
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• 2020

The characteristics of particulate matter made from daily cooking at a Korean residential apartment house with three dwellers had been investigated for about 3 months. All data were recorded by an optical particle counter every minute at the kitchen. Types of cooking such as boiling, frying, and grilling that performed in the house were listed. Boiling only was used in 32% cases among total 234 meals. Frying and grilling were 14% and 11%, respectively. From an initial indoor particulate matter smaller than 10 ㎛ in diameter, the increases due to cooking are reported by size. In case of boiling, PM at 1-10 ㎛ size and under 1 ㎛ size little increased. Normally, particles from oil or combustion in a process of frying or grilling increased indoor PM. In a case of grilling, particle mass concentration in a region of 1-10 ㎛ in diameter increased as much as 295 ㎍/㎥. Mass concentration of particles smaller than 1 ㎛ increased as much as 33 ㎍/㎥.

• Journal of Biosystems Engineering
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• v.26 no.5
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• pp.475-480
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• 2001

Sensing soil organic matter is crucial for precision farming and environment friendly agriculture. Near infra-red(NIR) was utilized to measure the soil organic matter. Multivariate calibration methods, including stepwise multiple linear regression(MLR), principal components recession(PCR) and partial least squares regression(PLS), were applied to soil spectral reflectance data to predict the organic matter content. The effect of soil particle size and water content was studied. The range of soil organic matter contents was from 0.5 to 11%. Near infrared (NIR) region from 700 to 2,500nm was applied. For uniform soil particle size, result had good correlation (R$\^$2/ = 0.984, standard error of prediction= 0.596). The effect of soil particle size could be eliminated with 1st order derivative of the NIR signal. However. moist soil had a little lower correlation. R$\^$2/ was 0.95 and standard error of prediction was 0.94% using the PLS method. The results showed the possibility of soil organic matter measurement using NIR reflectance on the field.

• Journal of Environmental Health Sciences
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• v.32 no.6
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• pp.533-538
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• 2006

The nano-particles are known to influence the environmental protection and human health. The relationships between transient vehicle operation and nano-particle emissions are not well-known, especially for diesel passenger vehicles with DPF(Diesel Particulate Filter). In this study, two diesel passenger vehicles were measured on a chassis dynamometer test bench. The particulate matter (PM) emission of these vehicles was investigated by number and mass measurement. The mass of the total PM was evaluated using the standard gravimetric measurement method, and the total number concentrations were measured on a ECE15+EUDC driving cycle using Condensation Particle Counter (CPC). According to the investigation results, total number concentration was $1.14{\times}10^{11}$M and mass concentration was 0.71mg/km. About 99% of total number concentration was emitted during the $0{\sim}400s$ because of engine cold condition. In high temperature and high speed duration, the particulate matter was increased but particle concentration was emitted not yet except initial engine cold condition According to DPF performance deterioration, the particulate matter was emitted 2 times and particle concentration was emitted 32 times. Thus DPF performance deterioration affects particle concentration more than PM.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.76-82
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• 2015

Active matter는 스스로 에너지를 가지고 움직이는 개체들이 서로 상호작용을 하여 집단적인 움직임을 보이는 물질이다. Rotational noise model은 반발 상호작용과 rotational noise를 가지고 있는 self-propelled particle로 active matter를 설명한다. 이 연구에서는 rotational noise model에서의 상 도표와 클러스터 형성을 알아보고 입자들의 집단적인 운동을 도와주거나 방해할 수 있는 leader 입자와 seed가 클러스터의 운동에 어떠한 영향을 주는지 알아본다.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.719-725
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• 2020

Objectives: With the growth of national interest in fine particulate matter, many complaints about pollutants emitted from air pollution emitting facilities have arisen in recent years. In particular, it is thought that a large volume of particulate pollutants are discharged from workplaces that use Solid Refuse Fuel (SRF). Therefore, particulate contaminants generated from SRF were measured and analyzed in this study in terms of respective particle sizes. Methods: In this study, particulate matter in exhaust gas was measured by applying US EPA method 201a using a cyclone. This method measures Filterable Particulate Matter (FPM), and does not consider the Condensable Particulate Matter (CPM) that forms particles in the atmosphere after being discharged as a gas in the exhaust gas. Results: The mass concentration of Total Suspended Particles (TSP) in the four SRF-using facilities was 1.16 to 11.21 mg/Sm3, indicating a very large concentration deviation of about 10 times. When the fuel input method was the continuous injection type, particulate matter larger than 10 ㎛ diameter showed the highest particle size fraction, followed by particulate matter smaller than 10 ㎛ and larger than 2.5 ㎛, and particulate matter of 2.5 ㎛ or less. Contrary to the continuous injection type, the batch injection type had the smallest particle size fraction of particulate matter larger than 10 ㎛. The overall particulate matter decreased as the operating load factor decreased from 100% to 60% at the batch input type D plant. In addition, as incomplete combustion significantly decreased, the particle size fraction also changed significantly. Both TSP and heavy metals (six items) satisfied the emissions standards. The measured value of the emission factor was 38-99% smaller than the existing emissions factor. Conclusions: In the batch injection facility, the particulate matter decreased as the operating load factor decreased, as did the particle size fraction of the particulate matter. These results will help the selection of effective methods such as reducing the operating load factor instead of adjusting the operating time during emergency reduction measures.

• Journal of Korean Society of Water and Wastewater
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• v.17 no.4
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• pp.534-541
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• 2003

Membrane processes are now frequently considered for application in drinking water treatment. The biggest impediment for applying membrane processes is fouling that comes from mass flux (such as particle and organic matter) to the membrane surface and its pores due to convection flow through the membrane. Natural organic matter (NOM) has been reported as the most detrimental foulant. Some research also indicated that particles were often the dominant cause of fouling. Therefore, both NOM and particle fouling need to be examined to better understand fouling in ultrafiltration. Two waters from natural sources, Lake Austin water and Missouri River water, were selected. Both waters are relatively hard waters but has significantly different particle concentrations, which will elucidate effects of particles on membrane fouling. Precipitative softening is traditionally designed to remove hardness ions in hard waters but it can also remove particles and organic matter. Therefore, the integrated water treatment with softening and ultrafiltration is proposed as a promising option for hard waters. The three levels of softening were used to represent different degrees of pretreatment to ultrafiltration in terms of organic matter (i.e., NOM fouling) and precipitates (i.e., particle fouling by further precipitation). Results showed that natural particles in Missouri River water was detrimental foulants of ultrafiltration. As the levels of softening were increased, NOM and particle removal was increased, and thus fouling was decreased. Direct images of the surface of the membranes by scanning electron microscopy allowed observation of the different properties of particles caught in fibril networks of natural organic matter.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.39-53
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• 2018

The purpose of this study was to investigate the conceptual structure of subjects related to matter having pre-service elementary school teachers by applying semantic network analysis (SNA). The analyzed concepts in the subjects of matter were 6 words such as 'atom', 'molecule', 'ion', 'electron', 'matter' and 'particle'. The results of SNA of the concepts are as follows : 1. In the semantic network of 'atom', words having a high betweenness centrality were linked with the words based on both the scientific context and the everyday context. 2. The network of 'molecule' was analyzed to be more organized than the network of the 'atom'. 3. In the network of 'ion', the group of words of the scientific context was distinguished from the group of words of the everyday context. 4. The network of 'electron' was analyzed to be more oriented on electricity and magnetism in the field of physics. 5. In the network of 'matter', the words related to compounds were linked with knowledge of history of science. 6. The network of 'particle' was not structured with words based on particulate nature of matter.

• Journal of Astronomy and Space Sciences
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• v.34 no.2
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• pp.153-159
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• 2017

The Standard Model of particle physics was established after discovery of the Higgs boson. However, little is known about dark matter, which has mass and constitutes approximately five times the number of standard model particles in space. The cross-section of dark matter is much smaller than that of the existing Standard Model, and the range of the predicted mass is wide, from a few eV to several PeV. Therefore, massive amounts of astronomical, accelerator, and simulation data are required to study dark matter, and efficient processing of these data is vital. Computational science, which can combine experiments, theory, and simulation, is thus necessary for dark matter research. A computational science and deep learning-based dark matter research platform is suggested for enhanced coverage and sharing of data. Such an approach can efficiently add to our existing knowledge on the mystery of dark matter.

• Environmental Engineering Research
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• v.20 no.3
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• pp.237-245
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• 2015

Particulate matter in indoor environments has caused public concerns in recent years. The objective of this research is to explore the influence of radiators on particle size distributions and concentrations. The particle size distributions as well as concentrations above radiators and in the adjacent indoor air are monitored in forty-two indoor environments in Xi'an, China. The temperatures, relative humidity and air velocities are also measured. The particle size distributions above radiators at ten locations are analyzed. The results show that the functional difference of indoor environments has little impact on the particle size distributions above radiators. Then the effects of the environmental parameters (particle concentrations in the adjacent indoor air, temperatures, relative humidities and air velocities) on particle concentrations above radiators are assessed by applying multiple linear regression analysis. Three multiple linear regression models are established to predict the concentrations of $PM_{10}$, $PM_{2.5}$ and $PM_1$ above radiators.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.10
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• pp.737-747
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• 2009

This study is to investigate the effect of the moisture, volatile matter and particle size in the coal on the pulverized coal combustion characteristics using CFD. The results show that as the moisture content in coal increases, flame temperature decreases because of heat loss driven from latent heat of vaporization and reduction of heating value. As the volatile matter content in the coal increases, the temperature in the region near the burner increases, while the temperature in rear region of boiler decreases. The solution to keep the temperature in the rear region of boiler is suggested that particle size is needed to be larger. As the particle size increases, the temperature in the rear region of boiler show tendency to increase, for combustion burning time of coal could be extended.