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dc.contributor.authorTsai, Hung-Chiehen_US
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dc.description.abstract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。 本研究主要目的是探討在已知污染源且至少兩個以上相似污染源情況下的結果,利用自製樣本進行模擬並比較污染源指紋及貢獻量的推估。因應實際採樣中人為因素所增添的誤差,故將四條河川樣本的量測誤差項納入考慮,但是要先驗算物種的量測誤差是否會大過於物種的濃度值,驗算過後有發生此情形者,此物種不放到受體模式PMF中運算。zh_TW
dc.description.abstractReceptor 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.en_US
dc.description.tableofcontents摘要 I Abstract II 目錄 III 圖目錄 V 表目錄 VI 第一章 前言 1 1.1 研究緣起 1 1.2 研究目的 2 1.3 研究架構 2 第二章 文獻回顧 3 2.1 懸浮微粒特性描述 3 2.2 受體模式相關理論 7 2.2.1 化學質量平衡(Chemical Mass Balance) 8 2.2.2 正矩陣因子法(Positive Matrix Factorization) 9 2.2.3 受體模式之相關研究及應用 13 第三章 研究方法 17 3.1 污染源追蹤元素之判定 17 3.2 PMF模式之應用 20 3.2.1 建置有效的濃度資料 20 3.2.2 模式結果的說明 27 3.3 模擬樣本的使用 29 第四章 結果與討論 33 4.1 自製模擬樣本之探討 33 4.1.1 中部地區四個污染源無共線性 34 4.1.2 兩個污染源存在共線性(選擇19個物種) 37 4.1.3 兩個污染源存在共線性(選擇23個物種) 41 4.1.4 小結 45 4.1.5 三個污染源存在共線性 46 4.2 模擬中部四條河川相似污染源 50 4.2.1 中部四條河川未考慮量測誤差 51 4.2.2 中部四條河川考慮量測誤差 55 第五章 結論與建議 61 5.1 結論 61 5.2 建議 62 參考文獻 63 附錄A 72zh_TW
dc.titleUsing PMF as a Receptor Model to Resolve Dust Sources in Central Taiwanen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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