| Summary: | 碩士 === 臺灣大學 === 生物環境系統工程學研究所 === 98 === A wildfire is one of issues to seriously damage the ecological environment and human health. Due to the impact of global warming and the foehn at Santa Ana, the frequency of wildfire occurrences is increasing in recent years. This study used PM2.5/PM10 ratios and meteorological variables to estimate the PM2.5 spatiotemporal distribution before and after the fire, in order to compare the quality caused by the wildfire at south California during Oct.21 ~Oct.29,2007.
In this study, Bayesian maximum entropy method and geographical weighted regression are used. Bayesian maximum entropy method can account for both certain and uncertain information to improve the accuracy of estimation. The Geographically Weighted Regression model is applied to modify the traditional regression model, which cannot capture spatial variations, and to solve the spatial non-stationary.
The results show that the relative error and the r-square during the period of the wildfire are 8.35μg/m3 and 0.27, respectively. The low r-square can result from the extreme events of PM2.5 during the period. The spatial distribution maps show that the higher concentration of PM2.5 occurred in San Diego and Los Angles, which is accord with the smoke shown in the satellite images.
The study applied BME method to assimilate the empirical relationship of PM2.5 derived by GWR and uncertain information. More information is required to account for the extreme events caused by the fires.
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