博碩士論文 105326019 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:11 、訪客IP:3.144.123.8
姓名 翁子芩(ZIH CHIN-WENG)  查詢紙本館藏   畢業系所 環境工程研究所
論文名稱 2017年臺灣 年臺灣 六個城市 六個城市 PM2.5金屬元素污染 金屬元素污染 來源推估 來源推估 研
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摘要(中) 氣膠金屬元素常可作為污染源排放的指標成分,本文使用2017年「細懸浮微粒(PM2.5)化學成分監測及分析計畫」的數據,探討台灣北(板橋)、中(忠明)、南(斗六、嘉義、小港)、東(花蓮)六個測站的PM2.5質量濃度和金屬元素成分濃度的時間變化並推論污染來源。研究中先以金屬元素富集因子(Enrichment Factor, EF)結合金屬元素判定係數,簡略推論污染來源;接著以金屬元素比值、鑭系元素三角圖、雙變量條件機率函數(Conditional Bivariate Probability Function, CBPF),推估煉油廠、燃油燃燒、交通排放和船舶污染的污染事件。然後以正矩陣因子法(Positive Matrix Factorization, PMF)和條件機率函數(Conditional Probability Function, CPF)定量推估污染來源,最後並綜合彙整各推論方法的異同。
結果顯示, PM2.5質量濃度由高而低依序為小港站>斗六站>嘉義站>忠明站>板橋站>花蓮站,總金屬元素濃度高低順序也是大略如此。六站都以Na、K、Fe、Ca、Al、Mg、Zn為高濃度元素,嘉義站的Pb、Ba、Ga和小港站的Na、Fe、Al、Mg、Zn濃度在四季都是六站最高;斗六站和嘉義站共通的金屬元素眾多,Tl、Rb和Cs在忠明站、斗六站和嘉義站的空間分布具有同源性。
各站都明顯受到來自交通排放源的影響,板橋、斗六、小港、花蓮站還受到燃油燃燒源的影響。小港站南方及東南方明顯受到煉油廠排放影響,發生煉油廠事件時La/PM2.5占比明顯升高,SO2小時值也會有明顯的高濃度。小港站及花蓮站當風向來自港口,明顯受到船舶污染排放影響,發生船舶事件的日平均風速高,V/Ni和La/Ce元素比值符合船舶污染範圍,SO2濃度隨而上升的次數多,峰值也較明顯。
彙整金屬元素判定係數、金屬元素比值、PMF三種推估方法結果顯示,六站普遍受到交通排放、礦物工業和生質燃燒影響,板橋站、忠明站、嘉義站和小港站的鋼鐵工業排放明顯,斗六站和花蓮站受到燃煤燃燒和燃煤發電廠影響,忠明站、嘉義站、小港站和花蓮站則受到燃油燃燒影響。
摘要(英) Aerosol metal elements frequently act as tracers in source identification. The study uses the data of “PM2.5 chemical composition monitoring and analysis study” in 2017 to investigate the variations of PM2.5 mass concentration, metal element compositions at the six stations of Banqiao (BQ), Zhongming (ZM), Douliu (DL), Chiayi (CY), Xiaogang (XG), and Hualien (HL). Enrichment factor (EF) coupling with the coefficient of determination between pairs of the metal elements was first adopted to infer source contributions. Secondly, the ratios of the selected metal elements, lanthanoid triangular plots, and Conditional Bivariate Probability Function (CBPF) were for the inferences on the contributions from the refinery, fuel burning, traffic emissions, and ship emissions. Thirdly, Positive Matrix Factorization (PMF) combining with Conditional Probability Function (CPF) was used to quantify source contributions.
The results showed that PM2.5 mass concentrations of the monitoring stations varied from high to low in the order of XG > DL > CY > ZM > BQ >HL. The high to low order of the total metal element concentrations were roughly the same. The dominant metal elements in mass concentration are Na, K, Fe, Ca, Al, Mg, and Zn. The average concentrations of Pb, Ba, and Ga at the CY station and Na, Fe, Al, Mg, and Zn at the XG stations were the highest of each element in the six stations. In terms of spatial source distribution, the metal elements with similar contributing sources for DL and CY stations were the most in the six stations. The metal elements of Tl, Rb, and Cs were with similar contributing sources at the ZM, DL, and CY stations.
All stations were obviously under the influences of winds from traffic emissions. The BQ, DL, XG, and HL stations were additionally subject to the influences of fuel oil burning. As for the influence from oil refinery emissions, the south and southeast winds to the XG station were responsible for it. The daily average of La/PM2.5 ratio increased much when affected by oil refinery emissions. Meanwhile, the hourly values of SO2 also went high. Since the XG and HL stations near a harbor, ship emissions affect them significantly when the winds coming from the harbor. The metal elemental ratios of V/Ni and La/Ce were in the ranges of ship emission characteristics, and SO2 concentration rose accordingly with evident peak values when under the influence of ship emissions.
In summarizing the results from the methods of determination coefficient, the metal elemental ratio, and PMF source apportionment, all the six stations were significantly affected by traffic emissions, mineral industry, and biomass burning. In particular, the steel industry influenced the BQ, ZM, CY, and XG stations evidently. The DL and HL stations were under the influences of coal combustion and coal-fired power plants, while the ZM, CY, XG and HL stations were subject to the influences of fuel oil combustions.
關鍵字(中) ★ 細懸浮微粒(PM2.5)
★ PM2.5質量濃度
★ PM2.5金屬元素成分
★ 金屬元素比值
★ 金屬元素污染來源推論
關鍵字(英) ★ PM2.5
★ PM2.5 mass concentrations,
★ PM2.5 metal elements
★ metal elements ratio
★ source inferences of metal elements
論文目次 目錄
摘要 I
Abstract II
目錄 V
圖目錄 IX
表目錄 XI
第一章 前言 1
1.1 研究緣起 1
1.2 研究目的 2
第二章 文獻回顧 3
2.1 細懸浮微粒 (PM2.5)的重要性 3
2.1.1 PM2.5對於環境的影響 3
2.1.2 PM2.5對於人體的影響 3
2.2 PM2.5形成機制 4
2.3 PM2.5組成來源 5
2.3.1 PM2.5水溶性離子 5
2.3.2 PM2.5碳成分 6
2.3.3 PM2.5金屬元素 7
2.4 金屬元素比值污染來源推估 9
2.5 台灣PM2.5時間與空間濃度分佈特性 18
2.6 受體模式正矩陣因子法 (PMF) 20
2.7 雙變量條件機率函數 (CBPF) 21
2.8 氣膠物種光譜輻射傳輸模型 (SPRINTARS) 22
第三章 研究方法 24
3.1 研究架構 24
3.2 採樣時間與地點 25
3.2.1 採樣地點概述 26
3.2.2 採樣時間 27
3.3 PM2.5手動採樣儀器 27
3.3.1 MetOne SASS PM2.5成分濃度採樣儀器 27
3.3.2 MetOne E-FRM PM2.5質量濃度採樣器 28
3.4 PM2.5質量濃度和化學成分分析方法 29
3.4.1 採樣濾紙前處理 29
3.4.2 質量濃度秤重之分析 30
3.4.3 水溶性離子分析方法 31
3.4.4 氣膠金屬元素成分檢驗分析方法 31
3.5 正矩陣因子法PMF (Positive Matrix Factorization) 38
3.5.1 輸入資料處理 39
3.5.2 PMF操作流程 39
3.6 條件機率函數CPF(Conditional probability function)推估方法 43
3.7 雙變量條件機率函數CBPF(Conditional bivariate probability function) 44
3.8 風速風向濃度散布圖 44
3.9 富集因子方法EF (Enrichment Factor) 45
3.10發散係數分析CD (Coefficient of divergence) 45
第四章 結果與討論 47
4.1 PM2.5和金屬元素濃度變化趨勢 47
4.1.1 PM2.5和金屬元素濃度時間變化 47
4.1.2 PM2.5和金屬元素濃度及空間季節變化 52
4.1.3 金屬元素濃度判定係數和發散係數分析CD (Coefficient of Divergence) 59
4.2 金屬元素EF值和金屬元素判定係數推估污染來源 63
4.2.1 金屬元素EF值 63
4.2.2 金屬元素判定係數 66
4.2.3 各站EF值>10金屬元素及判定係數探討 78
4.3 各站特定金屬元素比值及污染來源推估 78
4.4 鑭系金屬元素與V推估煉油廠來源貢獻 84
4.4.1 板橋站 85
4.4.2 忠明站 88
4.4.3 斗六站 90
4.4.4 嘉義站 93
4.4.5 小港站 95
4.4.6 花蓮站 98
4.5 污染來源事件推估 101
4.5.1 煉油廠事件日 101
4.5.2 燃油燃燒及交通污染 105
4.5.3 船舶污染推估 113
4.6 PMF受體模式推估PM2.5污染來源 121
4.6.1 板橋站污染來源推估 122
4.6.2 忠明站污染來源推估 127
4.6.3 斗六站污染來源推估 132
4.6.4 嘉義站污染來源推估 137
4.6.5 小港站污染來源推估 142
4.6.6 花蓮站污染來源推估 147
4.6.7 各站PMF推估因子貢獻比例 152
4.7 各站污染來源推估彙整及方法比較 152
第五章 結論與建議 156
5.1結論 156
5.2建議 157
第六章 參考文獻 158
附錄一 2017年各測站整年及季節風花圖 188
附錄二 2017年各測站PM2.5和氣體污染物CBPF 194
附錄三 PM2.5和氣體污染物CBPF污染區間明顯方向的工業來源 200
附錄四 六站金屬元素風向風速濃散布圖 202
附錄五 非船舶事件氣象因子 222
附錄六 風場模擬圖 226
附錄七 測站地圖 227
附錄八 PMF受體模式檢測結果 230
附錄九 台灣周遭船舶地圖 231
附錄十 台灣兩大水泥公司年產量 231
附錄十一 各站金屬元素成分占比(PM2.5及總金屬元素) 232
附錄十二 口試委員意見答覆 244


圖目錄
圖3.1.1研究架構圖 24
圖3.2.1採樣地點板橋、忠明、斗六、嘉義、小港、花蓮地理位置 25
圖3.3.1 MetOne SASS PM2.5採樣儀器採樣配置 28
圖3.3.2 MetOne E-FRM PM2.5採樣器內部構造示意圖 29
圖3.5.1美國環保署 PMF 5.0版操作流程 40
圖3.5.2使用轉軸工具修正結果操作流程 40
圖3.5.3使用限制(constraint)工具修正結果操作流程 41
圖4.1.1板橋站金屬元素濃度和PM2.5質量濃度時間變化圖 49
圖4.1.2忠明站金屬元素濃度和PM2.5質量濃度時間變化圖 49
圖4.1.3斗六站金屬元素濃度和PM2.5質量濃度時間變化圖 50
圖4.1.4嘉義站金屬元素濃度和PM2.5質量濃度時間變化圖 50
圖4.1.5小港站金屬元素濃度和PM2.5質量濃度時間變化圖 51
圖4.1.6花蓮站金屬元素濃度和PM2.5質量濃度時間變化圖 51
圖4.1.7各站金屬元素總濃度和PM2.5質量濃度判定係數 52
圖4.2.1板橋站PM2.5金屬元素EF值 64
圖4.2.2忠明站PM2.5金屬元素EF值 64
圖4.2.3斗六站PM2.5金屬元素EF值 65
圖4.2.4嘉義站PM2.5金屬元素EF值 65
圖4.2.5小港站PM2.5金屬元素EF值 65
圖4.2.6花蓮站PM2.5金屬元素EF值 65
圖4.4.1板橋站La, Ce, Sm 三角圖 87
圖4.4.2板橋站La/Sm vs La/Ce散布圖 87
圖4.4.3板橋站La, Ce, V 三角圖 88
圖4.4.4忠明站La, Ce, Sm 三角圖 89
圖4.4.5忠明站La/Sm vs La/Ce散布圖 90
圖4.4.6忠明站La, Ce, V 三角圖 90
圖4.4.7斗六站La, Ce, Sm 三角圖 92
圖4.4.8斗六站La/Sm vs La/Ce散布圖 92
圖4.4.9斗六站La, Ce, V 三角圖 93
圖4.4.10嘉義站La, Ce, Sm 三角圖 94
圖4.4.11嘉義站La/Sm vs La/Ce散布圖 95
圖4.4.12嘉義站La, Ce, V 三角圖 95
圖4.4.13小港站La, Ce, Sm 三角圖 97
圖4.4.14小港站La/Sm vs La/Ce散布圖 98
圖4.4.15小港站La, Ce, V 三角圖 98
圖4.4.16花蓮站La, Ce, Sm 三角圖 100
圖4.4.17花蓮站La/Sm vs La/Ce散布圖 100
圖4.4.18花蓮站La, Ce, V 三角圖 101
圖4.5.1小港站La/PM2.5高占比事件日氣象因子 104
圖4.5.2花蓮站La/PM2.5高占比事件日氣象因子 104
圖4.5.3板橋站受基隆港船舶排放影響日空氣品質及風速 119
圖4.5.4板橋站受台北港船舶排放影響日空氣品質及風速 119
圖4.5.5小港站受高雄港船舶排放影響日空氣品質及風速 120
圖4.5.6花蓮站東北東風受花蓮港船舶排放影響日空氣品質及風速 120
圖4.5.7花蓮站東風受花蓮港船舶排放影響日空氣品質及風速 121
圖4.6.1板橋站PMF解析因子數與Qtrue / Qexp值關係 122
圖4.6.2板橋站PM2.5觀測濃度和PMF模式模擬濃度關係(n=61) 122
圖4.6.3板橋站PMF解析因子指紋 123
圖4.6.4板橋站CPF推估高貢獻污染源來源 126
圖4.6.5忠明站PMF解析因子數與Qtrue / Qexp值關係 127
圖4.6.6忠明站PM2.5觀測濃度和PMF模式模擬濃度關係(n=61) 127
圖4.6.7忠明站PMF解析因子指紋 128
圖4.6.8忠明站CPF推估高貢獻污染源來源 131
圖4.6.9斗六站PMF解析因子數與Qtrue / Qexp值關係 132
圖4.6.10斗六站PM2.5觀測濃度和PMF模式模擬濃度關係(n=61) 132
圖4.6.11斗六站PMF解析因子指紋 133
圖4.6.12斗六站CPF推估高貢獻污染源來源 136
圖4.6.13嘉義站PMF解析因子數與Qtrue / Qexp值關係 137
圖4.6.14嘉義站PM2.5觀測濃度和PMF模式模擬濃度關係(n=60) 137
圖4.6.15嘉義站PMF解析因子指紋 138
圖4.6.16嘉義站CPF推估高貢獻污染源來源 141
圖4.6.17小港站PMF解析因子數與Qtrue / Qexp值關係 142
圖4.6.18小港站PM2.5觀測濃度和PMF模式模擬濃度關係(n=61) 142
圖4.6.19小港站PMF解析因子指紋 143
圖4.6.20小港站CPF推估高貢獻污染源來源 146
圖4.6.21花蓮站PMF解析因子數與Qtrue / Qexp值關係 147
圖4.6.22花蓮站PM2.5觀測濃度和PMF模式模擬濃度關係(n=61) 147
圖4.6.23花蓮站PMF解析因子指紋 148
圖4.6.24花蓮站CPF推估高貢獻污染來源 151


表目錄
表2.3.1 OC/EC與char-EC/soot-EC比值關係 7
表2.3.2金屬元素污染來源及排放指標元素 10
表2.4.1金屬元素比值文獻 10
表3.2.1本文樣本採集季節 27
表3.4.1水溶性離子偵測極限 31
表3.4.2濾紙樣本第一階段微波消化升溫設定條件 33
表3.4.3濾紙樣本第二階段微波消化升溫設定條件 33
表3.4.4感應耦合電漿質譜儀分析設定參數 34
表3.4.5各站低於方法偵測極限(MDL)筆數及百分比 35
表3.4.6 PM2.5金屬元素成分資料準確度與精密度 (李等,2016) 37
表3.5.1 PMF濃度矩陣及微粒成分分析不確定矩陣計算方法 39
表4.1.1 53
表4.1.1 PM2.5及總金屬元素的季節變化 54
表4.1.2冬季各站金屬元素成分濃度 55
表4.1.3春季各站金屬元素成分濃度 56
表4.1.4夏季各站金屬元素成分濃度 57
表4.1.5秋季各站金屬元素成分濃度 58
表4.1.6 2017年各站金屬元素濃度判定係數變化 62
表4.1.7 2017年金屬元素濃度CD值變化(林,2019) 63
表4.2.8各站污染來源整理 67
表4.2.1金屬元素文獻 68
表4.2.2板橋站PM2.5金屬元素濃度之間的判定係數(R2) 72
表4.2.3忠明站PM2.5金屬元素濃度之間的判定係數(R2) 73
表4.2.4斗六站PM2.5金屬元素濃度之間的判定係數(R2) 74
表4.2.5嘉義站PM2.5金屬元素濃度之間的判定係數(R2) 75
表4.2.6小港站PM2.5金屬元素濃度之間的判定係數(R2) 76
表4.2.7花蓮站PM2.5金屬元素濃度之間的判定係數(R2) 77
表4.3.1各站特定金屬元素比值和可能污染來源 80
表4.3.2金屬元素比值文獻 81
表4.3.3金屬元素比值文獻 82
表4.3.4各站金屬元素比值 83
表4.4.1鑭系金屬元素比值及各種污染來源 85
表4.4.2板橋站La/Ce、La/Sm、La/V比值整年及季節變化 86
表4.4.3忠明站La/Ce、La/Sm、La/V比值整年及季節變化 89
表4.4.4斗六站La/Ce、La/Sm、La/V比值整年及季節變化 91
表4.4.5嘉義站La/Ce、La/Sm、La/V比值整年及季節變化 94
表4.4.6小港站La/Ce、La/Sm、La/V比值整年及季節變化 97
表4.4.7花蓮站La/Ce、La/Sm、La/V比值整年及季節變化 99
表4.5.1各站La/PM2.5高占比日期 101
表4.5.2各站煉油廠事件日PM2.5金屬元素成分(metal/PM2.5)占比變化、氣體濃度差異、金屬元素比值與可能污染傳輸方向 103
表4.5.3各站燃油燃燒和交通排放污染來源風向的金屬元素比值 108
表4.5.4各項污染來源金屬元素比值及相關文獻 109
表4.5.5板橋站各風向金屬元素比值、水溶性比值與氣體濃度差異 110
表4.5.6忠明站各風向金屬元素比值、水溶性比值與氣體濃度差異 110
表4.5.7斗六站各風向金屬元素比值、水溶性比值與氣體濃度差異 111
表4.5.8嘉義站各風向金屬元素比值、水溶性比值與氣體濃度差異 111
表4.5.9小港站各風向金屬元素比值、水溶性比值與氣體濃度差異 112
表4.5.10花蓮站各風向金屬元素比值、水溶性比值與氣體濃度差異 112
表4.5.11各站可能接收到船舶污染的風向 116
表4.5.12板橋站來自港口風向受到船舶污染日及一般日各項因子比較 117
表4.5.13忠明站來自港口風向受到船舶污染日及一般日各項因子比較 117
表4.5.14斗六站來自港口風向受到船舶污染日及一般日各項因子比較 117
表4.5.15嘉義站來自港口風向受到船舶污染日及一般日各項因子比較 117
表4.5.16小港站來自港口風向受到船舶污染日及一般日各項因子比較 118
表4.5.17花蓮站來自港口風向受到船舶污染日及一般日各項因子比較 118
表4.6.1板橋站各因子季節貢獻占比 125
表4.6.2忠明站各因子季節貢獻占比 130
表4.6.3斗六站各因子季節貢獻占比 135
表4.6.4嘉義站各因子季節貢獻占比 140
表4.6.5小港站各因子季節貢獻占比 145
表4.6.6花蓮站各因子季節貢獻占比 150
表4.6.7各站PMF推估污染來源因子比例 152
表4.7.1污染來源推估結果彙整 154
表4.7.2適合台灣的金屬元素比值文獻彙整 155
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指導教授 李崇德 審核日期 2019-8-22
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