| 摘要: | 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。
;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. |
