台灣地區大氣氣膠特性之研究-台北高雄地區單顆粒氣膠與混合相氣膠污染來源推估

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黃瓊慧(Chiung-Huei Huang ) 查詢紙本館藏

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論文名稱

台灣地區大氣氣膠特性之研究-台北高雄地區單顆粒氣膠與混合相氣膠污染來源推估
(Source apportionment of particles by single particle and average bulk aerosol analysis in Taipei and Kao-hsiung area)

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摘要(中)

在邁向已開發國家之際，工業急劇發展和交通流量大增是近年來台灣工業區和都會區空氣品質惡化的主要原因，空氣品質不良(PSI>100)粒狀物仍為主要污染物（環保署，1998, 1999, 2000），如何針對污染源類別採取有效的空氣污染防制策略，已成為國內外學者研究的重點。
本研究自西元1999年12月至2000年11月以蜂巢式套管採樣器採集粒徑小於2.5mm的細粒氣膠，採樣站選定環保署台北新莊站及高雄小港站，各站每季取得10個有效樣品，共獲得台北40個、高雄41個有效樣品，以比較一年四季典型輕工業都會區及重工業都會區氣膠污染特性。採得樣品進行單顆粒氣膠分析及混合相分析（bulk analysis），單顆粒氣膠以電腦控制掃描式電子顯微鏡(CCSEM)分析，可獲得單一顆粒19種元素的強度百分比組成：C, O, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn和Pb，並將元素組成數據以聚類分析、因子分析及絕對主成份分析推估可能污染來源。混合相分析氣膠的質量濃度、水溶性離子、OC、EC及金屬離子，所得結果除轉換成元素濃度百分比後與CCSEM分析結果進行比對，並以絕對主成份進行污染源貢獻量推估。
CCSEM元素組成數據使用統計聚類分析結果可將台北、高雄站分成約20類左右，碳平均強度佔總分析強度達74%，氧佔了12%，其他以Si(4.0%)、Al(2.5%)、S(1.7%)及Na(1.3%)較高外其餘元素皆低於1%以下，其中氣膠數較多的有6類；各類污染源中只包含純碳氧為最主要類別，台北站平均約佔35%，高雄站平均約佔40%。因子分析結果兩站都有6類主要污染來源，台北站包含：木材農廢燃燒及二次氣膠、工業污染、塵土及鍋爐燃燒，肥料、水泥及生物氣膠、鋼鐵工業等六類；高雄站6類中有五類與台北站相當，另一類則為海鹽及工業冶煉程序。將多變量統計分析推估污染來源結果與當地固定污染源排放資料做比對，證明污染來源推估結果的合理性。針對CCSEM成果，以使用聚類分析較因子分析、絕對主成份分析能具有更佳的解析度，可將污染來源解析至更細的類別。
混合相分析結果顯示，新莊地區PM2.5質量濃度以春季最高為46.6μg/m3；小港地區冬季最高為94.5μg/m3，其中又以OC、硫酸根及銨根離子為主要成份。絕對主成份污染源推估在台北站及高雄站各可獲得二個污染來源，台北站最大污染來源為二次氣膠、海水飛沫及機動車輛來源；高雄站最大污染來源為機動車輛、農廢燃燒、二次氣膠、工業排放及塵土。
整體而言，雖然單顆粒氣膠與混合相分析方法不同，但是兩種方法解析的元素含量排序大致相同，兩種分析方法可以互補不足，對於大氣氣膠污染來源推估有所助益。

摘要(英)

Toward a developed country, Taiwan’s air quality is degraded by a fast development of industry and a steady increase of traffic flow. Particulate matter is still a predominant pollutant for bad air quality (PSI>100) (Taiwan EPA, 1998, 1999, 2000). An effective air pollution control strategy specific to pollution source becomes a focus point of research activities.
This study used honeycomb denuders to collect PM2.5 (particles with aerodynamic diameter smaller than 2.5mm) at Sin-chun site in Taipei County and Hsiao-kun site in Kaohsiung City from December in 1999 to November in 2000. In total, 40 samples collected in Taipei and 41 in Kaohsiung for a comparison in aerosol characteristics between a light and a heavy industrial site. Both single-particle analysis (using computer controlled scanning electron microscope, CCSEM) and bulk chemical analysis techniques were adopted for particle chemical compositions. Nineteen elements, namely C, O, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, and Pb, were identified from single particles selected randomly by CCSEM. Statistical techniques such as cluster analysis, factor analysis, and absolute principal component analysis (APCA) were applied to the elemental data from single particles to apportion their source contributions. In contrast, the chemical species of particles resolved from bulk analysis were converted into elemental composition for a comparison with CCSEM data and were apportioned their source contributions using APCA.
Among the detected elements, in terms of signal intensity, carbon is the most abundance element with an average of 74%, oxygen is second to carbon with an average of 12%, silicon is the third most abundance element with an average of 4%, those followed were aluminum 2.5%, sulfur 1.7%, and sodium 1.3%. Six of twenty source types resolved from cluster analysis were with significant particle numbers. Notably, the fraction of particle numbers for the source type with carbon and oxygen only is around 35% in Taipei and 40% in Kaohsiung, respectively. In addition, factor analysis shows that Taipei and Kaohsiung are similar in source contributions with industrial sources, sea-salt spraying, cement and fertilization production, dust and mixed burning sources, and vehicle emissions in common. The apportioned source types are consistent with sources shown in local emission inventory. In general, for elemental data from CCSEM, cluster analysis has the best resolution in source identification than factor analysis and APCA.
The bulk analysis shows spring PM2.5 at 46.6 mg/m3 is the highest among the four seasons at Sin-chun site. In contrast, the highest PM2.5 for Hsiao-kun site is at 94.5 mg/m3 for winter season. Major compositions of PM2.5 are OC, sulfate, and ammonium ion. The APCA indicates a mixed source of secondary reactions, sea-salt spraying, and motor vehicle emissions is predominant at Sin-chun site; whereas the mixed source from motor vehicle emissions, agricultural burning, secondary reactions, industrial activities, and resuspended dusts is the largest source type.
In summary, although single-particle analysis and bulk analysis are two different techniques, both methods are comparable in the rank of the resolved elemental abundance. These two methods are complimentary in source apportionment of atmospheric aerosols.

關鍵字(中)

★ 單顆微粒分析
★ 大氣氣膠
★ 污染來源推估
★ 混合相分析
★ 電腦控制掃描式電子顯微鏡

關鍵字(英)

★ atmospheric aerosols
★ source apportionment

論文目次

目錄
摘要……………………………………………….………………………….Ⅰ
目錄…………………………………………….…….………….…………….Ⅴ
圖目錄……………………………………..……………….…….………..Ⅷ
表目錄…………………………………………………………………………Ⅹ
1第一章前言I
2第二章文獻回顧4
2.1細粒徑氣膠對人體的危害4
2.2單顆粒氣膠微量分析(Microanalysis)5
2.3CCSEM(Computer-controlled Scanning Electron Microscopy)……………………………………………………………9
2.4單顆粒氣膠污染來源推估研究14
2.4.1聚類分析(Cluster Analysis, CA)15
2.4.2主成份分析法(Principal Component Analysis, PCA)18
2.4.3因子分析(Factor Analysis, FA)19
2.4.4絕對主成份分析(Absolute Principal Component Analysis, APCA)21
2.5混合相分析22
3三章研究方法24
3.1採樣地點及採樣時程規劃24
3.2細微粒氣膠的採集28
3.2.1採樣器28
3.2.2使用濾紙及濾紙前處理30
3.2.3樣品的運送及保存31
3.3樣品分析31
3.3.1利用CCSEM分析單顆粒氣膠31
3.3.2氣膠化學混合相分析33
3.4數據分析36
3.4.1聚類分析36
3.4.2因子分析38
3.4.3絕對主成份分析39
4第四章結果與討論46
4.1CCSME資料分析及污染來源推估47
4.1.1CCSEM資料分析47
4.1.2聚類分析51
4.1.3因子分析62
4.1.4絕對主成份分析70
4.2氣膠污染來源推估與驗證75
4.2.1季節性變化與污染來源推估結果75
4.2.2三種多變量統計分析方法的差異性80
4.2.3北高兩地污染來源推估結果鑑定82
4.3混合相化學分析結果及污染來源推估93
4.3.1混合相分析結果93
4.3.2絕對主成份分析及污染來源推估115
4.4CCSEM分析單顆粒氣膠與混合相分析結果比較125
4.4.1各分析元素的比較125
4.4.2污染來源推估結果比較132
5第五章結論與建議135
參考文獻140
附錄A
附錄B

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指導教授

李崇德(Chung-Te Lee)

審核日期

2001-7-13

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