大氣次微米微粒衍生性二元酸之生成及粒徑分布變異探討(II)

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標題:	大氣次微米微粒衍生性二元酸之生成及粒徑分布變異探討(II) Formation and size distributions of secondary dicarboxylic acids in atmospheric submicro aerosols (II)
作者:	蔡瀛逸
貢獻者:	環境工程與科學系
關鍵字:	大氣氣膠二元有機酸軌跡模式 atmospheric aerosol dicarboxylic acids trajectory model
日期:	2005
上傳時間:	2008-07-31 14:37:45 (UTC+8)
出版者:	台南縣：嘉南藥理科技大學環境工程與科學系
摘要:	本研究探討台南郊區之大氣氣膠無機鹽類及二元有機酸之組成特性變異，結果顯示台南郊區夏季及秋季氣膠無機鹽類濃度均以 SO4 2-、NO3 -及NH4 +光化產物為最大量，而秋季高污染時期氣膠NO3 -＞SO4 2-，與在夏季與秋季非高污染時期為SO4 2-＞NO3 -不同，且氣膠 NO3 - 濃度表現在農廢燃燒時期與高污染時期，佔PM2.5 質量比例分別為19.6%及18.0%。在高污染時期氣膠二元有機酸日夜間濃度高低與夏季相同，依序為oxalic acid＞succinic acid＞maleic acid，然而高污染時期二元有機酸之濃度較夏季時期高出約2-3 倍。農廢燃燒時期，由主成份分析發現NH4 +、NO3 -、SO4 2-、 oxalic acid、K+和Cl-的高相關負荷，顯示二元有機酸之來源貢獻與光化產物相似，皆為二次污染；而此時期K+和Cl-之濃度佔PM2.5 質量比例分別為1.6%及3.3%，與秋季非高污染時期及秋季高染時期相比，其濃度比例明顯上升，且oxalic acid 與K+及Cl-的相關係數亦較其他空氣品質時期更高，顯示農廢燃燒時期除了 K+與Cl-的明顯貢獻外，氣膠中亦存在來自生物源燃燒產生oxalic acid 的貢獻。在台南郊區之氣膠無機鹽類與二元有機酸組成之濃度粒徑分布，由夏季的單峰或雙峰，轉變成秋季的三峰及更多波峰的形態，高污染時的二元有機酸最大濃度波峰集中於0.19-0.32 µm 的 condensation mode，顯示高污染時期氣膠有更明顯的二元有機酸膠凝及光化產物生成貢獻。此外，氣膠succinic acid (C4)及malonic acid (C3)之最大濃度波峰與oxalic acid (C2)不同，秋季非高污染時期及高污染時期之oxalic acid 最大濃度波峰往更小粒徑位移，顯示秋季氣膠 oxalic acid 是經由C4和C3二元有機酸光化反應後之最後產物。此外，以空氣品質高斯軌跡傳遞係數模式(Gaussian trajectory transfercoefficient model, GTx)探討氣膠污染貢獻來源發現以面源為最大，污染來源佔35%，其次為點源的貢獻量佔23%，而上風邊界濃度、高空沉降以及線源模式則分別佔了17%、14%及 11%，此結果對其台南空品區之管制策略可提供參考。 In this research, variations of characteristic compositions of the atmospheric inorganic salts and low-molecular-weight dicarboxylic acids (low-Mw DCAs) for Tainan suburban regions were studied. During summer and autumn, SO4 2-, NO3 - and NH4 + are the major inorganic species with higher NO3 - than SO4 2- during the autumn episodic period that is different from the higher SO4 2- than NO3 - during summer and the autumn non-serious pollution period. The NO3 - sol mass constitutes 19.6% of PM2.5 mass during the agricultural waste burning period and 18.0% during the autumn episodic period. During the autumn episodic period, variations of the daytime and nighttime low-Mw DCAs concentrations are similar to those observed during summer with oxalic acid being the most abundant followed by succinic acid and maleic acid. However, concentrations of low-Mw DCAs during the autumn episodic period are 2 to 3 time the concentrations during summer. Results of principal component analyses reveal that during the agricultural waste burning period, the high correlation loadings between NH4 +, NO3 -, SO4 2-, oxalic acid, K+ and Cl-, indicating that the sources for low-Mw DCAs are similar to those for photochemical products; both are considered as the secondary pollution. During the agricultural waste burning period, the concentration ratios of K+ and Cl- to PM2.5 mass are 1.6% and 3.3%, respectively, which are similar to those during the autumn non-serious pollution period and the autumn episodic period; concentrations of K+ and Cl- obviously increase during the agricultural 2 waste burning period. Oxalic acid during these periods is more correlated with K+ and Cl- than during other air quality periods demonstrating that in addition to K+ and Cl-, a large quantity of oxalic acid sol is also generated by burning agricultural wastes. For Tainan suburban region, the particle size distributions for both aerosol inorganic salts and low-Mw DCAs change from single peak or double peaks in summer to triple or multiple peaks in autumn. During the high pollution period, the maximum concentration peaks for low-Mw DCAs dominant in the condensation mode of 0.19-0.32 µm. This reveals that particle coagulation and photochemical products contribute to the observed aerosols during the high pollution period. Additionally, the aerosol succinic (C4) and malonic acids (C3) have different maximum concentration peaks from oxalic acid (C2). During the autumn non-serious pollution period and high pollution period, the maximum concentration peak for oxalic acid shifts toward the smaller range indicating that the autumn sol oxalic acid is an end photochemical product from C4 and C3 low-Mw DCAs. Using the developed aerosol transformation mechanisms of the Gaussian trajectory transfer-coefficient model (GTx), the source apportionment reveals the most dominant contribution comes form area sources (35%) and follows by point sources (23%), upwind boundary sources (17%), top boundary sources (14%) and line sources (11%). These results could provide a view to applying proper and effective strategies for improving air quality in Tainan air basin.
關聯:	計畫編號：NSC94-2211-E-041-010
显示于类别:	[環境工程與科學系(所)] 科技部計畫

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