2015~2017年台灣都會區細懸浮微粒(PM2.5)金屬元素濃度時間及空間變化

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林乃芸(Nai-Yun Lin) 查詢紙本館藏

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論文名稱

2015~2017年台灣都會區細懸浮微粒(PM2.5)金屬元素濃度時間及空間變化

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摘要(中)

細懸浮微粒(氣動粒徑小於或等於2.5 μm的粒狀物質, PM2.5)對於環境及民眾健康有重大的影響，本文分析「104-105年細懸浮微粒(PM2.5)化學成分監測專案工作計畫」及「細懸浮微粒(PM2.5)化學成分監測及分析計畫」於2015年7月至2017年12月在板橋、忠明、嘉義、斗六、小港、花蓮環保署空氣品質監測站採集的化學成分數據，探討PM2.5質量濃度及金屬元素季節變化趨勢、金屬元素可能來源、高PM2.5濃度(>35 μg m-3)與低濃度(<35 μg m-3)金屬元素占比差異，推論金屬元素健康效應；同時，使用正矩陣因子法(Positive Matrix Factorization, PMF)推估污染來源並結合風向及條件機率函數(Conditional Probability Function, CPF)輔助判別當地污染源貢獻。
研究結果發現板橋及花蓮站PM2.5質量濃度變化，都是春季最高，忠明為秋季，斗六、嘉義及小港則是冬季最高，濃度最低季節除了板橋是秋季最低外，其他測站都是夏季。於六個測站中，Al、Fe、Na、Mg、K、Ca、Zn屬於高濃度群，這些元素主要貢獻來源可能有塵土(Al、Fe及Ca)、海鹽(Na及Mg)、交通(Fe、Na、Ca及Zn)、煉鋼(Fe及Zn)、廢棄物焚化(Al、Fe、Ca及Zn)等。中間濃度群金屬元素多與人為活動有關，如：燃煤、燃油等工業污染源及交通活動，低濃度群則與工業鍋爐排放和化石燃料燃燒有關。值得一提的是，釩元素(V)在各站各季節都是以小港站濃度最高，V是燃油的特徵元素，表示小港站在各站中受燃油燃燒的貢獻最高。
在採集的金屬元素中，Ni、Cr、Cd、As為國際癌症研究署歸類的第一級人類致癌物，Pb為第二B級人類致癌物，本文計算這些金屬元素吸入途徑的人類暴露濃度，推估結果以Pb為各站所有元素暴露量最高，Ni與Cr次之，Ni、Cr、Cd暴露濃度以小港站較高，As、Pb則為嘉義站，元素致癌風險數值介於10-5~10-7，各站以小港站的致癌風險較高，值得注意。
PMF受體模式結合CPF推估顯示，各站以燃油或燃煤燃燒、交通排放、工業排放、塵土、海水飛沫為主。比較採樣期間各站高PM2.5濃度與低濃度的成分占比，發現六站中只有Ba的占比在高濃度時略高於低濃度的現象，Ba的來源可能有輪胎和剎車磨損、鋼鐵廠及塵土，其他在低濃度時有較高占比的元素為Na、Mg、Ca、Ti、Mn、Ni、V，主要為塵土及工業污染的指標物種。
金屬元素在PM2.5濃度占比雖低，但它們往往攜帶著污染來源特徵，少數金屬元素又有致癌性，因此，PM2.5金屬元素的解析有其重要性。

摘要(英)

PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) plays a significant role in the environment and public health. This study analyzed the data collected at the Banqiao, Zhongming, Douliu, Chiayi, Xiaogang, and Hualien stations in the “2015-2016 PM2.5 chemical composition monitoring and analysis study” and “PM2.5 chemical composition monitoring and analysis study” in 2017. The objectives included the investigations on the variation trends of PM2.5 mass and metal element concentrations, potential sources of metal elements, differences of metal element proportions in high and low PM2.5, and the assessments of the health risk of metal elements. Additionally, this study executed source apportionments using Positive Matrix Factorization (PMF) and verified local source contributions by coupling Conditional Probability Function (CPF) with wind direction.
The results showed that the Banqiao and Hualien stations were with the highest PM2.5 seasonal concentrations in spring, while the Zhongming station was in autumn and the Douliu, Chiayi, and Xiaogang stations were in winter. In contrast, the lowest PM2.5 seasonal concentration was in autumn for the Banqiao station and summer for other stations. Among the six stations, Al, Fe, Na, Mg, K, Ca, and Zn belonged to the high concentration group. Major contribution sources of these metal elements can be derived from crustal material (Al, Fe, and Ca), sea salt (Na and Mg), transportation activities (Fe, Na, Ca, and Zn), steel refinery (Fe and Zn), and waste incineration (Al, Fe, Ca and Zn). The metal elements in the medium concentration group were mostly associated with anthropogenic activities, for example, coal and oil burning related industrial sources and transportation activities. For the metal elements in the low concentration group, industrial boilers and fossil fuel burning were major contributing sources. The highest concentration of vanadium (V) in all stations and seasons was at the Xiaogang station. Consequently, the Xiaogang station is under the influence of emissions of oil burning significantly in all stations, as V is the tracer element of oil burning.
Among the analyzed metal elements, Ni, Cr, Cd, and As are classified into group 1 and group 2B human carcinogens for Pb by the International Agency for Research on Cancer. This study computed the human exposure via inhalation pathway to find that Pb exposure was the highest followed by Ni and Cr in all stations. The exposure of Ni, Cr, and Cd ranked the highest at the Xiaogang station, while As and Pb reached the highest at the Chiayi station. The cancer risk of the elements lied in the range from 10-7 to 10-5. The Xiaogang station is noted to have the highest cancer risk among all stations.
The combination of PMF with CPF on source apportionment of metal elements resulted in identifying sources of oil (coal) burning, transportation activities, industrial discharge, crustal materials, and sea salt. Comparing high PM2.5 concentration (>35 μg m-3) with low concentration (<35 μg m-3) samples, Ba was the sole metal element with slightly greater PM2.5 proportion. The sources of Ba included tire and brake wearing, steel-making, and crustal materials. In contrast, Na, Mg, Ca, Ti, Mn, Ni, and V were with greater PM2.5 proportion in the low PM2.5 samples. They are tracers of industrial pollution and crustal materials.
Although metal elements are low in PM2.5 mass proportion, they tend to carry over polluting source characteristics and few of them are carcinogens. Therefore, the analysis of PM2.5 metal elements is of importance.

關鍵字(中)

★ 細懸浮微粒(PM2.5)
★ PM2.5化學成分
★ PMF
★ 金屬元素
★ 健康風險

關鍵字(英)

★ PM2.5
★ PM2.5 chemical composition
★ PMF
★ Metal elements
★ Health risk

論文目次

摘要 I
Abstract III
致謝 V
目錄 VI
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 3
第二章文獻回顧 4
2-1 細懸浮微粒(PM2.5)的重要性 4
2-1-1 細懸浮微粒(PM2.5)對人體的危害 4
2-1-2 細懸浮微粒(PM2.5)對環境的影響 5
2-2 細懸浮微粒(PM2.5)形成機制 6
2-3 細懸浮微粒(PM2.5)的化學組成 7
2-3-1 PM2.5氣膠水溶性離子組成 7
2-3-2 PM2.5氣膠碳成分來源 8
2-3-3 PM2.5金屬元素成分 9
2-4 台灣都會區細懸浮微粒(PM2.5)特性 13
2-5 受體模式正矩陣因子法PMF (Positive Matrix Factorization)應用 15
2-6 PM2.5元素成分的危害性 18
第三章研究方法 20
3-1 研究架構 20
3-2 採樣地點與時間 21
3-2-1 採樣地點 22
3-2-2 採樣時間 24
3-3 PM2.5質量濃度採樣器 25
3-4 PM2.5質量濃度分析方法 27
3-4-1 質量濃度秤重分析 27
3-4-2 氣膠金屬元素成分檢驗分析方法 28
3-5 正矩陣因子法PMF (Positive Matrix Factorization) 35
3-5-1 輸入資料處理 36
3-5-2 PMF操作流程 37
3-6 條件機率函數CPF (Conditional probability function)推估方法 41
3-7 健康風險評估 42
第四章結果與討論 43
4-1 金屬元素濃度變化趨勢 43
4-1-1 金屬元素季節變化 43
4-1-2 金屬元素總濃度季節變化 53
4-1-3 金屬元素總濃度在PM2.5占比季節變化 58
4-1-4 個別金屬元素空間變化 69
4-2 金屬元素濃度相關性及發散係數分析 (Coefficient of divergence) 75
4-3 金屬元素健康風險評估 79
4-4 金屬元素比值與污染來源推估 81
4-5 PMF受體模式推估都會區PM2.5污染來源 84
4-5-1 板橋站污染來源推估 85
4-5-2 忠明站污染來源推估 93
4-5-3 斗六站污染來源推估 100
4-5-4 嘉義站污染來源推估 107
4-5-5 小港站污染來源推估 114
4-5-6 花蓮站污染來源推估 121
4-5-7 各個測站PMF推估因子貢獻比例比較 127
4-6 六站事件日元素探討 128
第五章結論與建議 134
5-1 結論 134
5-2 建議 136
參考文獻 137
附錄一事件成因 153
附錄二受體模式檢測結果 161
附錄三口試委員意見答覆 162

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指導教授

李崇德

審核日期

2019-1-29

推文