• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.434-443
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. In addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.4
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• pp.335-343
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• 2006

Indoor air pollution has been significantly aggravated due to hazardous pollutants emitted from petrochemical finishing materials in new apartments. Pollutants emitted into indoor environment have significant effects on the health of occupants, causing undesirable symptoms related to sick building syndrome such as headache, dizziness, difficulty in concentration, etc. Therefore, this paper attempted to investigate the reduction effect of bake out on VOCs emission in new apartments. Experiments were carried out in three households, one of which was naturally ventilated. The naturally ventilated showed the lowest indoor pollutant concentration and also showed the highest reduction rate by the use of bake out. The most desirable result was observed when the household was naturally ventilated after bake out. More detailed experiments are expected to be carried out afterwards on the prediction of reduction rate of each pollutant.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.5
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• pp.383-396
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• 1997

Numerical prediction of the pollutant dispersion over a two-dimensional hilly terrain is presented. The dispersion model used in the present work is based on the gradient diffusion theory and the finite-volume method on a non-orthogonal boundary-fitted grid system. The numerical model is validated by comparing the results with the available experimental data for the flat-floor dispersion within a turbulent boundary-layer. The numerical error analysis is performed based on the guideline of Kasibhatla et al.(1988) for the elevated-source dispersion in the flat-floor boundary layer having a power-law velocity and linear eddy-diffusivity profile. The influences of the two-dimensional hilly terrain on the dispersion from a continuously released source are numerically investigated by changing the emission locations and heights. It is found that the distributions of ground-level concentration are strongly influenced by the source location and the emission height. Hence, the terrain amplification factor is greatly enhanced when the pollutant source is located within a flow separation region. Dispersion from a source of short duration is also simulated and the duration time of the pollutant is compared at several downstream locations on a hilly terrain. The results of the numerical prediction are applied to the evaluation of environmental impacts due to the automobile exhausts at the seashore highway with a parallel mountain range.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.48-54
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• 2016

The prediction of various emissions from coal combustion is an important subject of researchers and engineers because of environmental consideration. Therefore, the development of the models for predicting pollutants very fast has received much attention from international research community, especially in the field of safety assessment. In this work, response surface method was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of a drop tube furnace (DTF) to predict the spatial distribution of pollutant concentrations as well as final ones. The distribution of carbon dioxide in DTF was assumed to have Boltzman function, and the resulted function with parameters of a high $R^2$ value facilitates predicting an accurate distribution of $CO_2$. However, CO distribution had a difference near peak concentration when Gaussian function was introduced to simulate the CO distribution. It might be mainly due to the anti-symmetry of the CO concentration in DTF, and hence Extreme function was used to permit the asymmetry. The application of Extreme function enhanced the regression accuracy of parameters and the prediction was in a fairly good agreement with the new experiments. These results promise the wide use of statistical models for the quantitative safety assessment.

• Advances in environmental research
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• v.7 no.2
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• pp.109-120
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• 2018

In the present study research air quality analyses for $PM_{10}$, were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The averages concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of $PM_{10}$ occurs generally in the night while the least concentration was found at the afternoon. Monthly concentrations of $PM_{10}$ showed highest value in August, while least value was found in January. The seasonal concentrations showed the least amounts in autumn while the highest amounts in summer. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions SPSS software. RMSE test showed that among different prediction models, stepwise model is the best option.

• Journal of computational fluids engineering
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• v.4 no.3
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• pp.12-20
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• 1999

The computations of the flowfield and pollutant dispersion over a flat plate and the Russian hills of various slopes are described. The Gaussian plume and the puff model have been used to calculate concentration of pollutant. The Reynolds-averaged unsteady incompressible Navier-Stokes equation with low Reynolds κ-ε model has been used to calculate the flowfield. The flow data of a flat plate and the Russian hills from Navier-Stokes equation solutions has been used as the input data for the puff model. The computational results of flowfield agree well with experimental results of both a flat plate and Russian hills. The concentration prediction by the Gaussian plume model and the Gaussian puff model also agrees flirty well with experiments.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.3
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• pp.156-168
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• 1991

The purpose of this paper is to develop a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations prediction in urban area (Seoul). For this, the influence of the meteorological parameters on the $SO_2$ concentrations is investigated by a statistical analysis of the 24-hr averaged $SO_2$ levels of Seoul area during 1989 $\sim$ 1990. The annual fluctuations of the regression trend, periodicity and dependence of the daily concentration are also analyzed. Based on these, a nonlinear regression transfer function model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• KIEAE Journal
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• v.7 no.5
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• pp.53-58
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• 2007

In order to predict the indoor air pollutant, the VOCs emission rate is used through small chamber in the design process. However, the small chamber method has limitations as the convective mass transfer coefficient, the most important factor when predicting VOCs contamination of indoor air, is different between the small chamber result and the measured data in the actual building. Furthermore, the existing studies which analyzed mass transfer coefficient in the small chamber were directed on the small chambers developed at the time and FLEC(Field and Laboratory Emission Cell), thus, are different from the current small chamber which has been changed with improvements. The purpose of this study is to determine the emission rate of pollutant in multi-layered building materials, and predict the indoor pollutant concentration through the CFD(Computational of Fluid Dynamics) and CRIAQ2 based on the mass transfer coefficient on singled-layered building material by using the current small chamber widely used in Korea. Futhermore, this study used the new convective mass transfer coefficient(hm') which indicates the existing convective mass transfer coefficient(hm) including VOC partition coefficient(k). Also, formaldehyde was selected as target pollutant.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.244-252
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• 2021

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. In this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. ¹³⁷Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.