Atmospheric Stability Classification of Dispersion Factor for Radiological Analysis at YONGGWANG Site in KOREA

摘要

Air pollution model have been studied by the IAEA, NRC, and EPA. The aim of this study is to develop a more efficient methodology of explaining the behavior of the atmosphere depended on the atmospheric stability. Generally, Lagrangian, Eulerian or Gaussian models have been used to explain the air pollution behavior in Nuclear Power Plants (NPPs). These models employ various methods to determine the stability of the atmosphere. In this study, various models and simulations are reviewed and the respective methodologies are applied to YONGGWANG site to identify benefits. In this paper, major atmospheric stability classification methods are evaluated with meteorological data obtained by the data acquisition system of the YONGGWANG site. To classify stability, Pasquill 's classification method is the most widely used on the basic of its good agreement in respect of synoptic scope under a steady state and rapid response time on stability by heating or cooling caused by daily insolation or nocturnal surface radiation. From a comparison of modeling calculation results, we find that horizontal and vertical standard deviations of the wind fluctuation method tend to classify night-time stable conditions as unstable conditions. The classification matrix tables for Vogt 's vertical temperature difference and wind speed are modified and applied for practical use at the YONGGWANG site. We also confirm that the methods using the bulk Richardson number and Vogt s modified graphs are very useful in classifying atmospheric category. Modified tables for delta T and wind speed U and the Richardson method were made by using the reasonable joint frequency distribution from Pasquil's method and other existing results. From the obtained results, the correlation coefficient (r) was equal to 0.857. It was confirmed that atmospheric stability can vary with monitoring site characteristics, height, and vertical difference between monitoring detectors, and the classification methodology itself.