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標題: 受體模式PMF解析中部地區揚塵來源之共線性
Using PMF as a Receptor Model to Resolve Dust Sources in Central Taiwan
作者: 蔡宏杰
Tsai, Hung-Chieh
關鍵字: 受體模式;PMF;揚塵;Dust
出版社: 環境工程學系所
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受體模式是以受體點為導向透過周界採樣資料與污染源組成來追溯污染源,常見的受體模式有1.化學質量平衡法CMB (Chemical Mass Balance);2.絕對主成分分析APCA (Absolute Principal Component Analysis); 3.正矩陣因子法PMF (Positive Matrix Factorization)。本研究主要是以受體模式PMF針對共線性的污染源來做探討,此模式是多變量分析中的一種,它利用樣本濃度和其不確定性,藉由加權最小平方法推估污染源。此方法有幾項優點:(1)免去搜集污染源指紋那樣龐大的資料而怕資料不完全無法有效分析。(2)PMF並不會跟傳統CMB模式在解析相似污染源時,發生共線性的問題,導致CMB無法有效判定污染源的特徵。(3)其他多變量分析會有負值產生,PMF加入非負值的限制條件進行求解,能夠較好去判斷分析結果。

Receptor models use numerical techniques to simulate the physical and chemical processes that affect air pollutants in the atmosphere. There are several receptor models include CMB (Chemical Mass Balance), APCA (Absolute Principal Component Analysis) and PMF (Positive Matrix Factorization). If sources have collinearity, factor analysis is generally applied by PMF (Positive Matrix Factorization). PMF combines concentration and uncertainty to estimate sources. PMF has some advantages: (1) not require so much source profiles. (2) will not have problems caused by high collinear sources. (3) contributions from sources are always positive unlike other multivariate analysis methods.

Using PMF resolve at least two or more similar sources. The samples are simulated and compared the sources of contribute and the amount of fingerprints. The additional erro which increase four rivers of measurement error into account. If measurement error is larger than concentration of species that will not be on the PMF model to resolve.
其他識別: U0005-2008200916022500
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