• Journal of computational fluids engineering
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• v.14 no.4
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• pp.62-66
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• 2009

The control of particle concentration distribution in a pipe with an orifice and a sheath air is numerically investigated. When using Eulerian approach, there is no great change in the concentration distribution by the shape of orifice and molecular diffusivity. As the Reynolds number becomes small, the effect of orifice on the concentration distribution is decreased. For small Reynolds number, the concentration distribution can be effectively controlled by using a sheath air. The effect of the sheath air on the concentration distribution is increased, as the Reynolds number becomes small.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.3
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• pp.345-358
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• 2014

For introducing the groundwater quality assessment using background concentration of groundwater, several methods had been studied to estimate the background concentration of groundwater and to suggest the background concentration of study area. Some methods such as Box whisker plot, Percentile and Cumulative probability distribution had been adopted to estimate background concentration, and it was evaluated that the Cumulative probability distribution method presents more reasonable background concentration because it can consider the data distribution. So we estimated the background concentration of study area using cumulative probability distribution method. We suggested the background concentration for each hydrogeology respectively in case hydrogeological water quality similarity is very low.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.32-40
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• 2021

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.

• Journal of Environmental Health Sciences
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• v.22 no.3
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• pp.64-71
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• 1996

Measurement of concentration and size distribution of TSP, ammonium, nitrate and sulfate were made from Mar., 1991. to June., 1992 in Seoul. The seasonal variation of concentration and size distribution of aerosols has been investiated. Aerosol were collected and size frationated by Andersen air sampler. Size classified samples were extrated with deionized water and analyzed for ammonium, nitrate and sulfate by ion chromatography. As the results of measurement, the average of concentration and MMAD(mass median aerodynamic diameter) were $118.58 \mu g/m^3$, and $2.77 \mu m$ for TSP, $1.92 \mu g/m^3$ and $1.35 \mu m$ for ammonium, $1.34 \mu g/m^3$ and $1.58 \mu m$ for nitrate, $8.52 \mu g/m^3$ and $2.15 \mu m$ for sulfate. The Seasonal variation of concentration and size distribution was observed for ammonium, nitrate and sulfate. The concentration peak of TSP was observed in coarse particles in spring and observed in fine particles in winter. The concentration's distribution of TSP, ammonium, nitrate and sulfate was observed bimodal type during all season.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.4
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• pp.80-91
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• 1982

To precict the suspended sediments which are 80% of total sediments in a flood disch- arge, an equation representing vertical distribution of sediment concentration was derived based upon the diffusion theory and the logalithmic velocity distribution function in the tubulent flow mechanism. The hypothesis that the uniform mass transfer is occurred at upper part along the center line of water depth, was established as a preconition to solve the problem. The theorecal and the observed values were compared. And the theoretical equation was modified to be fit the theoretical values the observed values. Observed results are as follow; 1) Equation 12) is the theoretical equation representing the vertical concentration distri- bution of suspended sedimenta 2) Rous&exonential type vertical concentration distribution equation shows signification errors near the water surface. But the equation 12) shows substation cocentration values near the water surface. 3) Equation 15) is the modified theoretical equation which is possible to predict the vertical concentration distribution of suspended sediments.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.9
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• pp.2271-2282
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• 1995

Numerical simulations for the effects of secondary flow and turbulence diffusion on the particle concentration distributions have been carried out for the single stage electrostatic precipitator. The electrohydrodynamic secondary flow, particle concentration distribution and collection efficiency have been evaluated as a function of dimensionless parameters such as Re, $N_{end}$, $P_{e}$ x. The results of simulations show that for increasing secondary flow intensity the concentration distribution is drastically deformed and collection efficiency is decreased which is more than due to turbulent diffusion.n.n.

• The Korean Journal of Ecology
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• v.13 no.3
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• pp.215-224
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• 1990

The distribution of Spirodela polyrhiza and Lemna aequinoctialis was investigated and the limiting factors on the distribution were analyzed. At 66 sites in Korea, the presense or absense of duckweed were recorded and the water was sampled. The temperature, hardness, pH and contents of N, , , Na, K, Ca, Cu, Mg, Zn, Fe, Ni, Mn and Cd of water sampled were analyzed. The results were as follows; It seemed that the limiting factors influencing on the distribution was water temperature and concentration of . The critical temperature of the distribution of Spirodela and Lemna was presented 19.5$^{\circ}C$ and concentration of was limiting factor only in the distribution of S. polyrhiza. L. aequinoctialis was distributed in lower temperature than S. polyrhiza and it seemed that the distribution of L. aequinoctialis was not be influenced by the concentration of .

• 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.

• KIEAE Journal
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• v.5 no.3
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• pp.41-48
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• 2005

Before occupation of an apartment housing, the builders are required to inform the test result of IAQ to the public. However, there is no simplified method to predict IAQ before measurement of pollutant concentration. In this study, a simplified way of predicting IAQ based on the distribution of indoor pollutant concentration is proposed. 7 different cases of air change rate have been simulated through CFD analysis to get the distribution ratio of each pollutant material and then simplified functions were used with CRIAQ1 values derived from CFD simulation to evaluate by comparing the influence of each material in the indoor pollutant concentration. Again, a lot of efforts which can improve the indoor air quality have been performed. Materials used in indoor space are labeled with their pollutant emission level. Installation of ventilation system in residential buildings will be regulated by a building codes sooner or later. But it is important to understand the fact that layout of walls, location or size of openings will influence the indoor air flow and pollutant concentration. And location of emitting material influences to indoor air pollutants distribution. But until now there is few recognition and consideration of these factors. Therefore, in this paper the effects of these factors is proved and some kind of guideline is made for designers after a comparison of typical apartment floor plan and a new type plan with their average pollutant concentration and its distribution of each room. CFD(Computational Fluid Dynamics) program was used to show the indoor air flow and pollutant concentration distribution. For this purpose, a typical $100m^2$ apartment floor plan was chosen as a case study model and several alternatives were reviewed to improve the IAQ performance. The simulation took place in the condition of natural ventilation through windows.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.281-286
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• 1999

This study was conduced to examine the monthly and seasonal variation of $SO_2$, TSP, $O_3$ concentration in Dongsamdong, Pusan coastal area. And the characteristics of air pollution of this area was compared to Kwangbokdong in Pusan and Taehadong in Ullungdo. Monthly mean concentration of $SO_2$ and TSP showed lower than Kwangbokdong, $O_3$ was higher than Kwangbokdong. In case of $SO_2$, seasonal variation of Dongsamdong was remarkabler thn Kwangbokdong and the concentration difference of early morning and daytime was higher than Kwangbokdong. Taehadong showed very lower concentration as background area. In case of TSP, Dongsamdong was lower concentration and smaller diurnal change than Kwangbokdong, Taehadong showed very lower concentration as backgound area. In case of $O_3$, Dongsamdong was 10ppb higher than Kwangbokdong at daytime maximum concentration, diurnal change of concentration was higher, too. In case of frequency distribution of concentration, $SO_2$, and TSP at Dongsamdong showed higher frequency in low concentration class and $O_3$ showed in high concentration class as compared with Kwangbokdong.