請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/5227
標題: 受體模式CMB與PMF之比較與驗證
A Study On the Comparison Of Two Receptor Models:Chemical Mass Balance Model and Positive Matrix Factorization Model
作者: 梁志鋒
Liang, Jyh-Feng
關鍵字: receptor model
受體模式
CMB
PMF
source profile
Asian dust storm
CMB
PMF
污染源組成
沙塵暴
出版社: 環境工程學系所
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摘要: 本研究應用化學質量平衡法(CMB)與正矩陣因子法(PMF)兩受體模式推估大甲地區污染來源。PM2.5和PM¬2.5~10的樣本數據各有33組,其中有4組樣本受到沙塵暴事件的影響,在研究中將嘗試利用受體模式予以解析。模式CMB是由Matlab程式語言撰寫,並利用條件指數(Codition Index)和π矩陣做為污染源組成共線性判定方法,其優點在於明確定義了共線性判定準則,並增加污染源組成選取之彈性,而PMF模式則採用美國環保署開發之軟體EPA PMF 1.1 。 比對兩模式推估的結果,在PM2.5污染貢獻量方面,兩模式皆解析出交通排放、農廢燃燒、硫酸銨、硝酸銨、地殼物質、焚化爐、燃油鍋爐等7個污染源,其中交通排放在PM2.5中貢獻量為最大,CMB模式推估的貢獻量約佔57 %,PMF推估的貢獻量約35 %,農廢燃燒、硫酸銨、硝酸銨則位居污染貢獻量的第二至第四順位,分別佔CMB與PMF模式中總貢獻量的44.3 %、48.4 %;在PM2.5~10污染貢獻量方面,兩模式皆有解析出交通排放、地殼物質、海鹽飛沫、硝酸銨、焚化爐、農廢燃燒等6個污染源,且解析結果皆以交通排放和地殼物質為前兩大污染源,CMB模式推估佔總貢獻量的74%,PMF模式推估則佔總貢獻量的61%。其中CMB模式對交通排放的貢獻量推估為52%仍高於PMF模式推估貢獻量的35%。至於沙塵暴的貢獻量只有CMB模式解析出,其貢獻量只有整體的3.4 %,而PMF模式則無解析出。 整體而言兩模式對於主要污染源的解析成果皆為ㄧ致,交通排放的貢獻量推估差異較大的原因可能是CMB模式所使用國外地區的污染源組成,該資料不完全適用於大甲地區交通排放特性;污染源貢獻量較小的焚化爐、地殼物質,兩模式推估結果之相關係數(r2)不高,原因可能是收集的污染源組成資料數據不完全以及缺乏區域性的污染源組成所致;而沙塵暴貢獻量在PMF模式中無解析出,原因是樣本數太少,故模式無法辨識出。
Two receptor models, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF), are applied to estimate the source contributions of TaChia area in this study. There are thirty three samples of PM2.5 and PM2.5~10 , respectively. And four samples are impacted by Asian Dust Storm. This research will analysis the source contribution of Asian Dust Storm by receptor model. CMB model is written by Matlab program language. Condition Index and π matrix are used to identify the collinearity of source profiles by CMB model. Their advantages are that collinearity of source profiles are defined definitely and source profiles can be chosen flexible. PMF model is used the EPA PMF 1.1 version developed by USEPA. The results of two models are compared. Vehicle emissions, vegetative burning, ammonium sulfate, ammonium nitrate, crustal materials, incinerator, oil-fired boiled are analyzed to the source contributions of PM2.5 by two receptor models. Vehicle emissions are the major source contributions of PM2.5, and it was estimated about 57 % and 35 % of PM2.5 by CMB model and PMF model. The second to fourth source contributions are vegetative burning, ammonium sulfate, ammonium nitrate, and they accounts for 44.3 % and 48.4 % of total source contributions to CMB model and PMF model, respectively. Six sources include vehicle emissions, crustal materials, marine spray, ammonium nitrate, incinerator, vegetative burning are resolved to the source contribution of PM2.5~10 by two receptor models. The results show that vehicle emission and crustal materials are primary and secondary source contributions of PM2.5~10. They accounts for 74 % and 61 % of total source contributions to PM2.5~10 according to the results obtained from CMB model and PMF model, respectively. Vehicle emissions estimated by CMB model are still 52 % of total source contribution higher than 35 % estimated by PMF model. The contribution of Asian dust storm is only resolved by CMB model, and it accounts for 3.4 % of total source contributions. Instead, PMF model can't resolve the contribution of Asian dust storm. In conclusion, the major sources identified by the two receptor models are the same. The reason why high differences of contributions to vehicle emissions may be the source profile collected from the foreign area and it is not proper for the characteristics of vehicle emissions for TaChia area. Incinerator and crustal materials are low percentages of total contributions and their regression coefficient(r2) are low of the results between CMB model and PMF model. The reasons may be due to the incompleteness of profiles and a lack of local-specific profiles. In addition, a lack of samples to Asian dust storm, PMF model can't resolve the source contribution of Asian dust storm.
URI: http://hdl.handle.net/11455/5227
其他識別: U0005-2808200613561900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2808200613561900
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