請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/5599
標題: 高高屏空品區臭氧與非甲烷碳氫化合物特性之探討
A Study on The Characteristics Of Ozone and Non-Methane Hydrocarbons in Kaohsiung and Pingtung Area
作者: 吳鴻澤
Wu, Hung Tze
關鍵字: Ozone
臭氧
Ozone Formation Potential
ANOVA
Data Mining
臭氧生成潛勢量
單因子變異數分析
資料採礦
出版社: 環境工程學系所
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摘要: 高高屏空品區由於重工業發展迅速,其臭氧污染常高於其他空品區,故本研究將探討非甲烷碳氫化合物與臭氧之特性。資料來源為高雄與屏東地區境內環保署光化監測站、空氣品質監測站,以及中央氣象局氣象監測站,分析高高屏空品區在2007-2008年間之監測資料。依光化測站濃度顯示,甲苯與乙烯濃度都較其他有機物比例高,但有其量並不一定有臭氧生成之實,所以本研究將欲了解高高屏地區非甲烷碳氫化合物對臭氧生成特性進行討論。 根據各空品測站逐時濃度值進行相關性分析,結果可將高高屏空品區劃分出三個臭氧污染特性相似區域,並以屏東林園工業區臭氧最為嚴重。各光化測站氣象因子對臭氧之關係,由盒鬚圖顯示氣溫與風速皆與臭氧成正比,且均以西風、西北風、與西南風等海風向為主,再依據氣象因子結果以臭氧為70ppb進行分級,並搭配單因子變異數分析進行檢定,顯示各項有機物於小港站並無顯著性差異;橋頭站則為鄰、對二甲苯、間二甲苯;潮州站以苯與乙烯。為找出影響臭氧之特徵性物種,因此挑選臭氧事件日之案例進行濃度與生成潛勢量分析,可發現臭氧控制主要以乙烯與丙烯為主,而鄰、對二甲苯與甲苯亦可取代烯烴類生成臭氧。且為瞭解特徵性物種對臭氧之影響,利用資料採礦進行定量分析,找出高日照之時間點對於哪些有機物控制可使臭氧降低。結果顯示小港站可針對乙烯、甲苯進行控制;橋頭站為丙烯;潮州站為鄰、對二甲苯作濃度控制,將可有效降低高臭氧形成。
As the rapid development of heavy industry in Kaohsiung and Pingtung area, it''s level of ozone pollution often higher than the other areas. So this study would discuss the characteristics of ozone and non-methane hydrocarbons. The source of research data in this study is from the air quality monitoring station, photochemical assessment monitoring stations (PAMs), and weather station in Kaohsiung and Pingtung area. Analyzed the data between 2007 to 2008 displayed that toluene and ethylene level were higher than other organisms, but it doesn''t mean ozone was produced. Therefore, the goal of this study was to investigate the relationship between non-methane hydrocarbons and ozone. According to correlation analysis, it''s classified into three regions of air quality in Kaohsiung and Pingtung area by hourly ozone concentration obtained from the monitering station. The results show that ozone pollution was the worst in Pingtung-Linyuan industrial zone. In the relationship between meteorological factor and ozone, the wind speed and temperature were both proportional to ozone by box plot, and the mainly wind direction was west and northwest. Further to rank by concentration of ozone at 70 ppb in meteorological factor result and combine the ANOVA analysis for organisms test. The results showed that the Xiaogang station was no significant, the Qiaotou station was significant for m,p-xlyene, o-xlyene, and the Chaozhou station has signific -ant for toluene and ethylene. In order to identify the influence of key species for ozone, therefore selected the cases to compare the ozone concentration and ozone formation potential, the result show that ethylene and propylene were the key species, and the m,p-xylene and toluene can also be generated to replace the olefin. By data mining in finding high sunshine of the time points as to which organisms could be control for reduction of ozone, the result showed that ozone reduce effectively at the Xiaogang station could be for ethylene and toluene, the Qiaotou station could be for propylene, the Chaozhou station could be for m,p-xlyene.
URI: http://hdl.handle.net/11455/5599
其他識別: U0005-0402201016393900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0402201016393900
顯示於類別:環境工程學系所

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