利用高時間解析度資料進行台北地區細懸浮微粒來源分析與探討該地區細懸浮微粒成份之垂直空間變異

Abstract

暴露到細懸浮微粒 (Fine particulate matter, PM2.5)可能會導致多重健康危害，嚴重則可能致死。為維護大眾健康，了解PM2.5之來源是有必要的。正矩陣因子法 (Positive Matrix Factorization, PMF)是常被用於研究污染來源的一種受體模式，本研究以PMF搭配高時間解析度資料進行分析，使用34天的小時值監測資料，樣本數共664筆。最終，本研究解析出台北地區的七個污染源，其中以硫酸銨/新鮮海鹽為最大污染源 (28.17%)，接著依序是硝酸銨/金屬製造 (25.53%)、非鐵冶金工業/老化海鹽 (15.47)、交通 (15.25%)、土壤 (7.32%)、船隻排放 (5.33%)以及煙火 (2.93%)。在台北地區，有超過一半的居民居住於三樓以上，另外，先前的研究指出，高架橋也是影響PM2.5分布的重要因素。因此，為了瞭解PM2.5在垂直空間的變異，以及交通因素對其影響，本研究選定鄰近高架橋的大樓作為樓層採樣的地點。所收集樣本測量PM2.5總重、16種元素、反射度以及3種離子。在垂直分布上，典型與長程傳輸有關的成分，沒有觀察到明顯的垂直變異，而與交通或是揚塵相關的成分，則會受到高架橋的影響。結果顯示交通排放是台北地區的重要污染來源並且可能會影響PM2.5成分的垂直分布。

Exposure to fine particulate matter (PM2.5) may cause multiple health hazards. In severe cases, PM2.5 may result in death. In order to protect public health, it is necessary to understand the sources of PM2.5. Positive Matrix Factorization (PMF) is a receptor model commonly applied for source apportionment. This study performed PMF with highly time-resolved data. The sampling duration was 34 days and the sample size of hourly data was 664. Finally, the study identified seven sources in Taipei, with Ammonia sulfate/ Fresh sea salt as the largest source (28.17%), followed by Ammonia nitrate/ Metal manufacturing (25.53%), Non-ferrous metallurgy/Aged sea salt (15.47%), Traffic (15.25%), Soil dust (7.32%), Ship emission (5.33%) and Firework (2.93%). In addition, this study selected a building adjacent to the viaduct as the sampling site to evaluate vertical variation of PM2.5. All the manual samples were analyzed for the concentrations of PM2.5, 16 elements, absorption coefficient and 3 ions. In the vertical distribution, apparent variation was not observed for the components associated with long-range transportation while the components associated with traffic or soil dust were affected by viaducts. The results show that traffic is an important source of PM2.5 in Taipei and may affect the vertical distribution of PM2.5 components.