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  • 學位論文

大氣中生物性微粒之時間及空間分布─以大台北地區為例

Spatiotemporal Distributions of Ambient Bioparticles in the Greater Taipei Area

指導教授 : 趙馨

摘要


空氣污染會造成多種健康危害,其中生物性微粒為重要的空氣汙染物 之一,可能會造成氣喘、過敏以及呼吸道疾病的增加。為了評估空氣汙染 對人體造成的危害,一些大型研究使用既有的測站監測值做為個人空氣汙 染的暴露指標,然而有研究發現大氣污染物的濃度在都市中的變異相當大, 因此近年來研究學者開始使用土地利用回歸分析來改善相關的暴露評估 問題。本研究針對大台北地區進行多點採樣,利用土地利用分析配合地理 資訊系統,建立大台北地區大氣中生物性微粒的濃度預估模式,以瞭解大 台北地區生物性微粒的時間及空間分布。本研究在大台北地區44 個採樣 點進行三季的空氣採樣,每季所有採樣點各進行24 小時的連續採樣,進 行真菌孢子及總細菌濃度的分析,並收集可能影響生物性微粒濃度的社會、 環境因子資料,建立統計預估模式。根據本研究的結果,大台北地區的優 勢真菌包括Ascospores、Aspergillius/Penicillium、Basidospores 及 Cladosporium;真菌孢子及總細菌濃度皆有明顯的季節變化,分別在春季 及冬季最高。根據多變項回歸分析,溫濕度較高、風速低、鄰近森林及農 地時,大氣中真菌孢子濃度較高,若鄰近人口密集區域,大氣中真菌孢子 濃度則較低;大氣中總細菌濃度則與相對濕度及細懸浮微粒呈顯著正相關。 未來的研究除可繼續探討不同社會及環境因子對生物性微粒的影響外,並 可配合相關健康資料來評估大氣中生物性微粒對人體健康的影響。

並列摘要


Biological particulates (bioparticles) are one of the most important air pollutants in ambient air. Ambient bioparticles may induce or exacerbate asthma, allergic diseases and respiratory diseases. Many large epidemiological studies used data from existing stationary monitoring stations as personal exposure surrogate to examine the health effects of air pollution. Due to large variation of pollutant levels in cities, researchers started to utilize land-use regression (LUR) analysis to better predict personal exposure levels. In this study, we conducted intensive multi-site bioparticle samplings in the Greater Taipei area and developed regression models using Geographic Information System (GIS) and LUR to predict the spatiotemporal variations of ambient bioparticles. We selected 44 representative sampling sites and collected air samples continuously for 24 hours at each sampling location for three seasons. Fungal spores and total bacteria were analyzed. Information on social and environmental factors related to bioparticles levels was also collected to develop predictive models. According to the results, the most prevalent fungal taxa in the Greater Taipei area included Ascospores, Aspergillius/Penicillium, Basidospores and Cladosporium. Fungal spore and total bacterial levels had significant seasonal variations, and were highest in spring and in winter, respectively. According to the results of multiple regression analyses, fungal levels were positively correlated to temperature, relative humidity, forest and farmland, and negatively to wind speed and highly populated area. The concentration of total bacteria had positive relationships with relative humidity and fine particles. Future studies can further evaluate the effects of other social and environment factors on bioparticles, and to examine the health impacts of bioparticles.

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