請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/5282
標題: 利用CMB與PMF模式針對不同共線性程度之污染源的分析與比較
A Study On The Comparison Of Different Collinearity In Source Profiles By Two Models: Chemical Mass Balance Model and Positive Matrix Factorization Model
作者: 王富民
Wang, Fu-Ming
關鍵字: Receptor model
受體模式
CMB
PMF
estimated sample
source profiles
CMB
PMF
模擬樣本
污染源組成
出版社: 環境工程學系所
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摘要: 空品模式分為(1)擴散模式(2)受體模式。針對污染來源分析之研究最常用的是受體模式,受體模式係利用統計的方法將所收集資料中氣象資訊配合在周界觀測的濃度來追溯污染源(Hopke,1985)。這類的模式利用觀測的質量濃度並配合質量守衡理論進行污染源的推估。若一地區之污染源的數量與特性為已知,則可採用傳統的CMB(Chemical Mass Balance)模式針對每一個污染源貢獻至每一樣本的貢獻量推估。但是其有缺點,(1)倘若地區污染源的相關資訊大部分為未知,例如缺少污染區域的污染源指紋資料,無法分析。(2)當污染源相似的情形下,有共線性的情形發生,CMB模式無法得到合理解。在這些情況下則可利用新發展的受體模式PMF進行分析。PMF(Positive Matrix Factorization),是多變量分析中的一種,利用樣本濃度和不確定性藉著有效變異加權最小平方法去推估污染源。不像傳統受體模式CMB需要污染源指紋資料。此方法有幾項優點:(1)免去搜集污染源指紋那龐大的資料 (2)沒有像傳統CMB在解析污染源相似的情形之下有共線性的問題,無法有效判定污染源及特徵因子。(3)不像其他多變量分析有負值產生,較好去判斷分析結果。 為了解受體模式中PMF對於共線性高的污染源判定效果以及CMB與PMF模式的差別與好壞,因此利用方程式去製作不同污染源指紋之樣本,使用兩種模式分別去比較和分析結果的優劣。結果顯示若是利用差異性大的指紋資料製作而成之樣本,兩模式無太大差異,但若製作樣本之指紋具有共線性時,CMB模式無法解析且誤差極大,PMF則依舊可以分析出差異性。顯示出PMF對於相似污染源判定性較CMB來的高。
URI: http://hdl.handle.net/11455/5282
其他識別: U0005-1308200719442800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1308200719442800
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