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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64302


    Title: 使用CMAQ-HDDM探討台灣地區臭氧之非線性 反應及估算高臭氧區的來源貢獻量: 2011年個案分析;Sensitivity analysis of ozone's nonlinearity and source contribution for a high ozone event in Taiwan: 2011 case study
    Authors: 伍孟璟;Wu,Meng-Ching
    Contributors: 大氣物理研究所
    Keywords: 臭氧;高階去偶合直接方法;Ozone;HDDM
    Date: 2014-04-08
    Issue Date: 2014-06-19 13:58:26 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 臺灣逐年上升的臭氧(O3)已成為重要的空氣污染議題。但O3並非直接排放,而是由前驅物氮氧化物(nitrogen oxides,NOx)與揮發性有機物(volatile organic compounds,VOC),經一連串光化反應而產生。由於O3形成機制複雜且非線性,透過模式進行敏感度分析 (如:改變前驅物的排放量),可以評估O3隨前驅物的時空分布變化。不同於以往的Brute Force Method (BFM),High-order Decoupled Direct Method (HDDM) 提供了一個更有效的方式進行敏感度分析。本篇研究使用多尺度空氣品質模式(Community Multi-scale Air Quality Modeling system)搭配高階去耦合直接方法(CMAQ-HDDM),探討2011年10月18至24日高臭氧汙染事件裡臭氧的非線性反應,以及估算在高臭氧區裡不同源區及源類別的貢獻量。
    由環保署資料顯示,此個案於南高屏地區觀測到的O3在23日及24日超標,為臭氧事件日,因此針對這兩天進行三組敏感度實驗。第一組實驗主要探討模擬範圍內,前驅物的擾動量對於O3的敏感性。第二組實驗主要討論源區與受區的關係,其中源區分成五個區塊來討論:雲林、嘉義、台南、高雄及屏東,進而評估各源區的汙染物對於南高屏地區O3的影響。第三組實驗則是在判別各種排放源類別對於O3生成之重要性。
    在模擬結果與觀測的比對裡,兩者大致相符,NOx和O3的RMSE結果分別為17.6及19.2 ppb。另外,HDDM和BFM的結果也很接近,所以CMAQ-HDDM的結果,具有相當的可信度。根據Base case風場分析發現: 23日南高屏地區風場呈現明顯停滯帶,而24日南高屏地區的局部環流明顯,風速也較前天高。
    在第一組實驗中:由一階敏感度係數的結果及O3等值線分析都顯示,鄰近源區(如:台北、台中、高雄)的地方因環境中NOx過多,由滴定效應為主,為VOC限制區;然而在下風處(如:屏東),則為NOx和VOC過渡帶。二階敏感度的結果則顯示在白天高臭氧區有顯著的非線性反應及前驅物之間的交互作用。在第二組實驗中:根據臭氧1小時及8小時最大值的貢獻量分析結果發現,23日高值出現於台南,污染源由當地排放為主。而24日由於風速較強,導致O3高值出現於屏東,污染源主要來自於高雄,而台南與高雄之間交互作用之貢獻量也不容小覷。在第三組實驗結果裡,人為源對於臭氧之敏感性較生物源來得明顯。進一步分析各類別源的對於臭氧之貢獻量發現,NOx以線源最主要之排放源,而VOC則是以面源最主要之排放源。此外,生物源及人為源之間的交互作用對於臭氧的貢獻量也有舉足輕重的影響(-10 ~ -12%)。
    總結此個案中的結果,我們發現O3高值發生時,受到大氣擴散作用的不同,O3前驅物的成份也有差異,此研究成果有助於我們瞭解如何有效的控管排放源,以減少大氣中的O3濃度。; Elevated O3 concentration has been an important environmental issue in Taiwan. O3 is a secondary air pollutant driven by photochemical reactions involving primary air pollutants such as volatile organic compounds (VOC) and nitrogen oxides (NOx, NO and NO2). Sensitivity analysis is vital for O3 due to the nonlinearity and complex reaction. Unlike Brute Force Method (BFM) where model simulations are repeated with different model inputs, HDDM offers an alternative by directly solving sensitivity equations derived from the governing equations of the model. Thus, HDDM provides an accurate and effective way to perform sensitivity analysis. In this study, the Community Multi-scale Air Quality Modeling system coupled with Higher-Order Direct Decoupled Method (CMAQ-HDDM) was applied for a high O3 event from October 18th to 24th 2011. Three sensitivity experiments were designed to investigate nonlinear response of O3 with respect to its precursor emissions and to quantify the emission contributions from different source regions and categories for the area where high O3 concentration occurs.
    The base-case model result is in a good agreement with the observation on meteorological and pollutant fields. The comparison between BFM and HDDM is in a similar pattern as well, providing a reliable HDDM simulation for further analysis. For the first sensitivity experiment, the aim is to explore how O3 respond to the precursor’s emission. Results indicate most of source region exhibited VOC-limited region, while over the downwind area, both NOx and VOC contributes for O3 production (NOx and VOC transition region). For the second sensitivity experiment, as the northeasterly wind prevailed throughout the event, five possible source region are assigned (including Yulin, Chiayi, Tainan, Kaohsiung, and Pingtung) to estimate the contribution where high O3 concentration occurs. There were stagnant conditions over southern Taiwan on Oct. 23rd, the highest O3 occurred near Tainan city, and most pollutants were from local emissions. However, the local circulation is more pronounced on Oct. 24th, and the highest O3 occurred in downwind area, Pingtung where the emission from Kaohsiung is the main contributor. Furthermore, the importance of various emission source categories is discussed. Among the anthropogenic emissions, the mobile NOx emission and area VOC emission mainly contributes for high O3 concentration. The cross effect between biogenic VOC emission and anthropogenic NOx is also a moderate contributor (about -10 ~ -12%).
    In conclusion, due to changes of the meteorological conditions, the emissions were redistributed on Oct. 23rd and 24th. The results from this study can support the policy makers to build an efficient control strategy for reducing O3 concentrations.
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