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Application of Positive Matrix Factorization Model for Examining Spatial Variations of Exposure to PM2.5 with Different Height in Taipei Metropolis
fine particulate matter,vertical variability,positive matrix factorization (PMF),elemental composition,
|Publication Year :||2017|
|Abstract:||暴露到細懸浮微粒 (fine particulate matter, PM2.5)可能會導致急性或慢性的健康危害。空間變異時常被應用在許多研究中去瞭解不同環境狀況對PM2.5的影響。然而，大部分有關空間變異的研究只有考量到水平變異的部分，關於垂直變異還沒有被全面性的了解。
本研究透過觀察細懸浮微粒在同一棟建築物但不同高度的變化來探討台北都會區之垂直變異。研究中根據不同的環境特色和建築物類型對五棟具有代表性的建築物進行空氣樣本的採集，建築物的樓層高度被分為三類:低樓層(一到三樓)、中樓層(六或七樓)和高樓層(十或十一樓)，採樣時間包含夏、秋、冬三季。樣本分析項目有PM2.5濃度、十六種元素個別濃度和吸收係數。本研究也透過正矩陣因子法 (positive matrix factorization model, PMF) 來觀察不同汙染源對垂直變異的影響。
透過秤重後所得之PM2.5濃度來觀察樓層變化可以發現，在低樓層有最高的數值其次為高樓層及中樓層。濃度分別為 15.59 μg/m3、15.25 μg/m3和15.04 μg/m3。以PMF模式解析之汙染源圖譜(source profile)結果可推估主要受到的汙染源影響有:衍生性氣膠/長程傳輸、交通相關排放源、油漆工程、油類燃燒、塵土逸散源、鉻相關工業以及一個混合汙染源。其中最主要的貢獻來源為衍生性氣膠/長程傳輸 (48.71%)。利用模式結果來觀察不同汙染源對樓層的影響，結果發現大部分的來源都在低和高樓層都有較高的貢獻，其中大部分汙染源樓層貢獻之百分比相對誤差皆大於10%。本研究採集樣本時間跨越夏、秋、冬三季，因此也利用模式解析之數據探討季節性汙染源的貢獻變化。由解出的七個汙染源可以發現大部分的來源都在冬天有較高的貢獻量，其中以衍生性氣膠/長程傳輸 和 塵土逸散源最為明顯，分別在冬天貢獻了59% 和58%。最後，本篇研究提供有關汙染源在樓層上的貢獻以及好發季節之相關資訊，可以提供給政府作為日後擬定空氣汙染策略或污染源管制措施的參考。
Exposure to air pollutants such as fine particle matter (PM2.5) has high association with acute or chronic adverse health effects. Spatial variations have been examined and applied to evaluate air pollutant exposure in residential area widely. However, most of the past studies which examined spatial variation only considered about horizontal aspect. The vertical variations have not been studied extensively.
Examining the vertical variations in urban areas is essential to realize the source influences from different height. This study measured the vertical variations by sampling three categories of floors at typical buildings in Taipei metropolis. Five sampling buildings were selected by its environmental features, including different volume of traffic or the various surrounding objects such as viaduct or parking lots. The categorized floor-levels included low-level sampling site set from first to third floors, mid-level sampling sites set between the sixth and seventh floors, and high-level sampling sites set between the tenth and eleventh floors. PM2.5 samples were collected to analyze the mass concentrations, absorption coefficient and 16 elements concentrations in three seasons (summer, autumn and winter). Moreover, positive matrix factorization (PMF) model was utilized to estimate the sources influences of different floors.
The PM2.5 mass concentration was obtained by weighing before and after the sample collection. The highest value was at low-level floor (15.59 μg/m3), followed by high-level floor (15.25 μg/m3) and mid-level floor (15.04 μg/m3). On the other hand, based on the resolved source profiles and source contribution, seven characterized sources were identified: Secondary aerosol/ long-range transport, Traffic related, Paint project, Oil combustion, Dust source, Cr-rich industry and one mixed source. The largest contributor was secondary aerosol/ long-range transport (48.71%) in this study. Most of the vertical trends had higher value at low- and high- level floor, but lowest value at mid-level floor with 10% relative error. The seasonal variations of source contributions were analyzed in this study which showed that the highest value occurred in winter mostly. The source of secondary aerosol/long-range transport contributed 21%, 20% and 59% and dust source contributed 12%, 30% and 58%, respectively, in summer, autumn and winter.
Finally, the effect of sources emission at different floors and seasonal variations could be utilized as information for developing prevention strategies of air pollution.
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