Radioactive materials depositied after nuclear accident or radiological emergency result in radiation exposure to individuals living in long-term contaminated territories. Therefore, the remedial actions should be taken on affected areas for the evacuated residents to return to their homes and normal lifestyle. Meanwhile, radiation exposure occurs through various pathways by work types during the site clean-up. Therefore, dose assessment is crucial to protect emergency workers and helpers from the potential radiological risk. This study estimated the exposure dose to individuals decontaminating the areas contaminated with 60Co, 63Ni, 90Sr, 134Cs, 137Cs, and then calculated the maximum workable soil concentration to comply with the reference level of 20 mSv/y for transition to existing exposure situations. For the realistic assessment, the detailed exposure scenarios depending on the types of work (excavation, collection, transportation, disposal, landfill), and the relevant exposure pathways were used. In addition, with the LHS (Latin Hypercube Sampling) - PRCC (Partial Rank Correlation Coefficient) method, sensitivity analysis was performed to identify the influence of the input parameters and their variation on the model outcomes. As a result, the most severe exposure-induced type was identified as the excavator operation with an annual individual dose of 4.75E-01 mSv at the unit soil concentration (1 Bq/g), from which the derived maximum workable soil concentration was 4.21E+01 Bq/g. Dose contribution by isotopes were found to be 60Co (55.63%), 134Cs (32.01%), and 137Cs (12.28%), and the impact of 63Ni and 90Sr were found to be negligible. Dose contribution by exposure pathways decreased in the following order: ground-shine, soil ingestion, dust inhalation, and skin contamination. Furthermore, the most high sensitive input parameters and their PRCC were found to be as the dilution factor (0.75) and as the exposure time (0.63). In conclusion, the results are expected to contribute to optimize radiation protection strategeis for recovery workers and to establish appropriate response procedures to be applicable in areas with high deposition density after a radiological or nuclear emergency.

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.