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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/35947

    Title: 台灣地區地面臭氧濃度的長期模擬
    Other Titles: Long-term simulation of ground-level ozone concentrations in Taiwan
    Authors: 蔡增瑋;Tsai, Cheng-wei
    Contributors: 淡江大學水資源及環境工程學系碩士班
    江旭程;Chiang, Hsu-cherng
    Keywords: 光化學模式;長期模式模擬;模式評估;Photochemical model;long-term simulation;model evaluation
    Date: 2005
    Issue Date: 2010-01-11 07:22:53 (UTC+8)
    Abstract: 地面附近光化學污染所產生的臭氧問題為台灣地區極待解決的問題,而三維化學 傳輸模式為空氣品質規劃與管理的重要工具,此類模式計算費時,所以過去無法用於長期模擬。大都只選擇短期高濃度的事件日來做模擬,常以改變參數或邊界條件的方式來修改模擬結果,以期待得到較佳的模擬結果,但是此種模擬方式卻不一定能成功模擬其他狀況,因此本研究採用光化模式進行一個月連續之長期模擬,以涵蓋不同的天氣型態,且不加任何修正,以評估模式可達到的準確性。
    The ground-level ozone produced by photochemical air pollution is a series environmental problem in Taiwan. Three dimensional chemical transport model (3-D CTM) is an important tool for air quality planning and management. The computation of CTM is a time-consuming task; their applications to long-term simulations were prohibited in the past. The short periods with high concentration, the so-called “episode”, were selected for simulations. Sometimes, the models were well tuned by adjusting parameters or boundary conditions so as to obtain better results. However, the performance may be poor while these models were applied to other situations. In this research, we use a 3D CTM to carry out one-month continuous simulation in order to determine the performance the model under various meteorological conditions. The simulation use same parameters for all period.
    The performances of the calculated 1-h concentrations are: the unpaired-in-time peak prediction accuracy ±0.71; the normalized bias ±0.67; the gross error, <0.73. This is somewhat worse than the performance criteria suggested by USEPA. The performances of the calculated 8-h concentrations are: the unpaired-in-time peak prediction accuracy ±0.73; the normalized bias ±0.54; the gross error, <0.61. The uncertainties in the emission database can generate some errors on the predictions. The performance of this model can be improved if the reasons that cause some extraordinarily flawed results can be identified. Since this system is so complex, there are numerous opportunities for improvements in the future.
    Appears in Collections:[水資源及環境工程學系暨研究所] 學位論文

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