Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

This study was performed to investigate the single and combined effect of concentrations of garlic juice according to the pH and temperature on the growth of Salmonella enteritidis in brain heart infusion broth, and to develope Response surface model for the effect of concentrations of garlic extract. The results of electric conductibility of Salmonella enteritidis growing in the range of incubation temperature (25～42℃), pH (5.5～9.0) and concentration of garlic juice (0～0.8%) showed that a badge with high temperature had low D.T.value and concentration of garlic extract were significantly correlated with D.T.value (p<0.01). Growth of Salmonella enteritidis in the condition of 37℃ and pH 7.0 presented the lowest D.T.value.

This study was performed to plan pollutant loading allocation by sub-watershed at Kumho river basin located in the north Kyeongsang province. HEC-geoHMS which is extension program of ArcView was used to extract sub-watershed. To simulate water quality, Qual2eu model was calibrated and validated. BOD was simulated under several scenarios to evaluate reduction effects of pollutant loading. Uniform treatment and transfer matrix method was considered. Effects of headwater flow rate and efficiency waste water treatment plant were also considered.

To identify possible associations with concentrations of ambient air pollutants and daily mortality in Busan, this study assessed the effects of air pollution for the time period 1999-2000. Poisson regression analysis by Generalized Additive Model were conducted considering trend, season, meteorology, and day-of-the-week as confounders in a nonparametric approach. Busan had a 10% increase in mortality in persons aged 65 and older(95% CI : 1.01-1.10) in association with IQR in NO2(lagged 2 days). An increase of NO2(lagged 2days) was associated with a 4% increase in respiratory mortality(CI : 1.02-1.11) and CO(lagged 1 day) showed a 3% increase(CI : 1.00-1.07).

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.