본 연구를 통한 결론은 다음과 같다. 5개의 염색폐수배출업소에서 각각 3회씩 채취한 최종 방류수를 대상으로 한 생물독성시험 결과 미생물독성시험에서는 B시료와 E시료에서 독성이 관찰되었고 급성 물벼룩시험에서는 반수치사농도가 C시료에서만 관찰되어 염색폐수의 독성이 시험 생물종별로 다르게 나타날 수 있음을 제시하였다. 동일 생물종을 대상으로 실시한 급성 및 만성시험에서는 급성반수치사가 5개 방류수 중 C시료에서만 비교적 낮은 농도에서 관찰이 된 것에 비해 만

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.

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.