Moonhee Han

Numerical simulation for a long-range dispersion of a pollutant using Chernobyl data

A Lagrangian particle model has been developed and applied to a long-range atmospheric dispersion. The developed numerical model has been tested by comparing its predictions with the ^1^3^7Cs air concentrations recorded over European areas during the Chernobyl accident. Sensitivity studies were performed to investigate the numerical accuracy according to a variation of the parameters such as the mixing height and diffusion coefficient in the model. From a comparative study, the calculated concentration distributions were more sensitive to a variation of the mixing height than to the changes of the diffusion coefficient values. Also, the calculated concentrations agreed with the time series of the measured ones at some sampling points.

Radiological risk assessment caused by RDD terrorism in an urban area

This paper specifically discusses a radiological risk assessment due to RDDs (Radiological Dispersion Devices) containing Cs-137 in the metropolitan area of Seoul, South Korea. The comparison of an effective dose caused by airborne plume and deposited Cs-137 is performed with and without consideration of the wind direction. When the dose is computed conservatively, an effective dose is around twice that of a dose computed realistically. Monte Carlo simulations showed that the 95% confidence interval for morbidity was 2.40×10(-5) to 8.55×10(-5), and mortality was 3.53×10(-5) to 1.25×10(-4).

Statistical approaches to forecast gamma dose rates by using measurements from the atmosphere

In this paper, the results obtained by inter-comparing several statistical techniques for estimating gamma dose rates, such as an exponential moving average model, a seasonal exponential smoothing model and an artificial neural networks model, are reported. Seven years of gamma dose rates data measured in Daejeon City, Korea, were divided into two parts to develop the models and validate the effectiveness of the generated predictions by the techniques mentioned above. Artificial neural networks model shows the best forecasting capability among the three statistical models. The reason why the artificial neural networks model provides a superior prediction to the other models would be its ability for a non-linear approximation. To replace the gamma dose rates when missing data for an environmental monitoring system occurs, the moving average model and the seasonal exponential smoothing model can be better because they are faster and easier for applicability than the artificial neural networks model. These kinds of statistical approaches will be helpful for a real-time control of radio emissions or for an environmental quality assessment.

Analysis of atmospheric dispersion factors for building wakes at the Wolsung nuclear site in Korea

The characteristics of atmospheric dispersion considering the building effects in the Wolsung nuclear site in Korea were studied using ISC-PRIME and ARCON96 models. The maximum 2-h average atmospheric dispersion factor (ADF) was six times larger when the building geometry was considered in ISC-PRIME and two times larger in the exclusion area boundary. Owing to different adjustments for wind speed by the stability class, the ADFs calculated using ARCON96 were smaller than those calculated using ISC-PRIME. Strategies for locating buildings need to be considered to maximise dispersion when planning for constructing several reactors and accessory buildings at the Wolsung nuclear site.

Inter-Comparison Study of the ENSEMBLE Project

During the days of the Chernobyl accident, the European national long-range dispersion forecasts would differ because of differences in national models, and differences in weather prediction methods. ENSEMBLE project was launched for a reconciliation and harmonization of the disparate long-range dispersion forecasts. Responsible European emergency organizations in addition to Canadian, Japanese, Korean and US agencies have participated in ENSEMBLE. KAERI joined the inter-comparison study for the exercise on the 901–001 scenario in ENSEMBLE. KAERI was assigned KR1 as a national code and 53 as a model number. The model of KAERI was compared with the other models of the participants in ENSEMBLE. The comparative results are presented with the scatter plots and statistical methods in this paper.

Effect of the duration time of a nuclear accident on radiological health consequences.

This study aimed to quantify the effect of duration time of a nuclear accident on the radiation dose of a densely populated area and the resulting acute health effects. In the case of nuclear accidents, the total emissions of radioactive materials can be classified into several categories. Therefore, the release information is very important for the assessment of risk to the public. We confirmed that when the duration time of the emissions are prolonged to 7 hours, the concentrations of radioactive substances in the ambient air are reduced by 50% compared to that when the duration time of emission is one hour. This means that the risk evaluation using only the first wind direction of an accident is very conservative, so it has to be used as a screening level for the risk assessment. Furthermore, it is judged that the proper control of the emission time of a nuclear accident can minimize the health effects on residents.

Development of a radioactivity mapping tool to support the decision making for radiological emergency events

A radioactivity mapping program was developed to support the decision making in case of a radiological emergency event. Geostatistics and kriging methods were used in the program to make a more accurate radioactivity map for the polluted area. Two variogram models, i.e., linear and exponential models, were tested, and the exponential variogram model showed a better performance when compared with the linear interpolation for estimating unobserved data for radioactivity.