Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/5513
標題: 應用CMB受體模式分析懸浮微粒高污染事件之研究
作者: 賴沛君
關鍵字: CMB受體模式;高污染
出版社: 環境工程學系
摘要: 
摘 要
本研究主要應用CMB受體模式及利用污染源特徵元素經驗公式來推估台中地區中興大學測站,在2002年1月至2003年4月期間的高污染事件中,大氣懸浮微粒之污染源及貢獻量情形。這段期間高污染事件可分為農廢燃燒事件(N=2)、大陸沙塵事件(N=10)及一般高污染事件(N=1)等三類,實驗係利用雙粒徑分道採樣器(Dichot)進行日夜間粗細粒採樣,並分析氣膠中水溶性陰陽離子、元素碳和有機碳、以及元素成份。
CMB受體模式分析高污染事件之污染源貢獻量結果顯示,在農廢燃燒事件期間,細粒之污染來源由大至小依序為農廢燃燒(48.6%),交通(35.6%),硝酸鹽(8.4%),硫酸銨(5.1%),海鹽飛沫(0.5%)及電力業(0.1%)。而非農廢事件(N=8)部份,細粒污染源主要為交通(48.8%),硫酸銨(26.7%),農廢燃燒(22%),硝酸鹽(6.9%)及電力業(0.9%)。其中事件日之農廢燃燒污染源明顯高出非事件日2倍之多,躍升為第一大污染源。在大陸沙塵事件中,粗粒的污染源則依序為地殼物質(44.8%),交通(29.8%),硝酸鹽(7.9%),海鹽飛沫(5.8%)及硫酸銨(4.1%),其中地殼物質約有83%來自大陸沙塵。一般高污染事件期間,粗粒的污染源則依序為交通(42.3%),硝酸鹽(19.2%),海鹽飛沫(15.9%),地殼物質(14.4%)及硫酸銨(12.6%),細粒為交通(36.3%),硫酸銨(28.7%),硝酸鹽(16.2%)及電力業(0.5%),其二次光化物質與海鹽飛沫污染源貢獻量較其他事件日結果顯著提高,此兩者皆為一般事件日之主要污染源。
至於利用污染源的特徵元素經驗公式推估地殼物質(Al、Si、Ca、Ti、Fe、Mg、N和K)、海鹽飛沫(Na+)、硫酸銨(SO42-)及硝酸鹽(NO3-)的貢獻量方面,其中用Chan的經驗公式推估之污染源貢獻量與受體模式分析之結果相關性良好(R2=0.85~0.99),顯示Chan的經驗公式可適用於推估台中都會區懸浮微粒之污染源貢獻量。

Abstract
The purpose of this work is to apply a CMB receptor model and the mass reconstruction empirical formulas to estimate the source contributions to ambient aerosol particles. The particles were sampled during the episodic events at NCHU sampling station at Taichung from January 2002 to April 2003. The episodic events were grouped into three categories: biomass burning episodes (N=2), Asian dust storm events (N=10) and a general PM10 episode (N=1). In these events, PM2.5 and PM2.5-10 were collected during daytime and nighttime by using Dichotomous samplers. The samples were further analyzed for water soluble ions, elemental carbon, organic carbon and the metallic elements.
The results obtained from CMB receptor modeling showed that the pollution sources of PM2.5 during the biomass burning event included biomass burning (48.6%), vehicle exhaust (35.6%), nitrate (8.4%), ammonium sulfate (5.1%), marine spray (0.5%) and power plant (0.1%). However, during the non-biomass burning periods (N=8), the sources were vehicle exhaust (48.8%), ammonium sulfate (26.7%), biomass burning (22%), nitrate (6.9%) and power plant (0.9%). It clearly showed that the contribution from the biomass burning was the most significant pollution source during the event. The sources of PM2.5-10 during Asian dust storm events were geological material (44.8%), vehicle exhaust (29.8%), nitrate (7.9%), marine spray (5.8%) and ammonium sulfate (4.1%). Furthermore, there were approximately 83% of the geological material contributing from the dust-storm. The source apportionments of PM2.5-10 during the general PM10 episode were vehicle exhaust (42.3%), nitrate (19.2%), marine spray (15.9%), geological material (14.4%), and ammonium sulfate (12.6%). But the sources for PM2.5 were different, which included vehicle exhaust (36.3%), ammonium sulfate (28.7%), nitrate (16.2%) and power plant (0.5%). The results also showed that the secondary aerosol and the marine spray were the major sources for the general PM10 episodic event.
An additional method of mass reconstruction was used to estimate the source contributions from the geological material (Al, Si, Ca, Ti, Fe, Mg, N and K), marine spray (Na+), ammonium sulfate (SO42-) and nitrate (NO3-). The results showed that a good correlation (R2 = 0.85~0.99) was found between the estimation by using the Chan's empirical formula and the result obtained from the CMB modeling. Therefore it is reasonable to conclude that the source contributions of the ambient aerosol particles sampled at Taichung can be estimated by using the mass reconstruction method.
URI: http://hdl.handle.net/11455/5513
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