- 學位論文
北臺灣地區移動污染源細懸浮微粒空間濃度與族群健康風險評估
Spatial Health Risk Assessment Associated With The Fine Particulate Matter Emitted From Mobile Sources In Northern Taiwan.
摘要
北臺灣移動污染源佔人為PM2.5濃度貢獻度的40%，本研究使用American Meteorology Society/Environmental Protection Agency Regulatory Model (AERMOD)大氣污染擴散模式模擬2010年臺北、新北及桃園市的線源細懸浮微粒排放擴散分布，透過空間分析以及地理加權迴歸(Geographically Weighted Regression,GWR)找出濃度高低與各疾病死亡率及就醫率之正相關地區。 將臺灣排放量資料庫(Taiwan Emission Data System, TEDS)線排放源中的汽機車PM2.5排放作為主要污染源，氣象資料用中央氣象局之板橋站(46688)氣象測站資料，以及運用地理資訊系統(Geographic Information System, GIS)轉換後之地表特性參數，運行Aermod Meteorological Preprocessor (AERMET)後得到地表特性檔及大氣垂直剖面檔，輸入北臺灣地區高程檔(Digital Elevation Model, DEM)，再運行AERMOD後得到濃度分布圖，並透過空間分析做空氣污染與各疾病死亡率及就醫率的相關性分析，死亡率分析包含全癌症、心臟疾病(高血壓性疾病除外)、肺炎、高血壓性疾病、結核病、腎炎腎病症候群及腎病變、慢性下呼吸道疾病、慢性肝病及肝硬化、肝和肝內膽管癌、氣管支氣管和肺癌、腎臟癌、鼻咽癌、全死因、急性支氣管炎及急性細支氣管炎、流行性感冒與動脈粥樣硬化，共計十六項；就醫率分析包含惡性腫瘤(全癌症)、心臟疾病(高血壓性疾病除外)、肺炎、高血壓性疾病、結核病、腎炎腎病症候群及腎病變、慢性下呼吸道疾病、慢性肝病及肝硬化，共計八項。 以AERMOD模擬結果來看發現臺北市的PM2.5濃度模擬值以大同區(34.8 μg/m3)、中山區(31.2μg/m3)及中正區(28.3μg/m3)為最高，與排放源分布有關。在AERMOD模擬值與監測站實測值的比值以夏、秋兩季較準確，由於PM2.5的來源及成因眾多且難以量化，沒有單純監測線源的濃度值(不受外來PM2.5濃度影響)可以驗證，故在驗證上產生很多不確定性。 以本研究之線源排放濃度值去作空間分析發現各疾病與濃度正相關之地區，臺北市需注意之區域包含：中正、萬華、大同、中山、士林、北投區；新北市需注意之地區包含：新莊、林口、五股、蘆洲、三重、泰山、烏來、板橋、鶯歌、樹林、中和、土城、貢寮、金山、淡水、三芝、石門、八里、永和區；桃園市需注意之地區包含：桃園、中壢、楊梅、蘆竹、大園、龜山、八德、龍潭、平鎮、新屋、觀音及復興區，其中，正相關地區死亡率以心臟疾病及高血壓性疾病為最多；就醫率以高血壓性疾病及腎炎腎病症候群及腎病變為最多。 本研究藉由地理加權迴歸分析PM2.5濃度值上升所造成每十萬人口死亡率及每十萬人口就醫率的影響，有別於簡單線性迴歸結果的全區域負相關，地理加權迴歸分析幫助本研究瞭解PM2.5濃度在空間上的影響，以空間上地區的相關性提供本研究探討正相關影響之各區。
並列摘要
Mobile sources accounted for approximately 40% of the anthropogenic contribution of PM2.5 concentration in northern Taiwan. This study aims to evaluate the associations between area-cause-specific mortality and morbidity and concentration of fine particulate matter (PM2.5) that emitted from mobile sources in northern Taiwan, namely, Taipei City, New Taipei City, and Taoyuan using air dispersion model-American Meteorology Society/Environmental Protection Agency Regulatory Model (AERMOD) and Geographically Weighted Regression (GWR). Emission data, weather data, and measurement of ambient PM2.5 were obtained from Taiwan Emission Data System Version 8.1, Central Weather Bureau and Environmental Protection Adminstration, respectively. Land surface characteristics were analyzed using Geographic Information System. These data were further used to estimate the spatial concentrations of PM2.5 using AERMOD. Associations between mortality from all cancer, heart disease, pneumonia, hypertensive diseases, tuberculosis, nephritis, chronic lower respiratory diseases, chronic hepatitis, liver and intrahepatic bile duct cancer, lung cancer, nephritis cancer, nasopharyngeal cancer, all causes, acute bronchitis, influenza, and atherosclerosis, and outpatient visits of all cancer, heart disease, pneumonia, hypertensive diseases, tuberculosis, nephritis, chronic lower respiratory diseases, chronic hepatitis, and concentrations of PM2.5 were evaluated using generalized liner model and GWR in GIS. This study compared the spatial concentrations of PM2.5 simulated by AERMOD and measurements from general air quality stations (n=11) of EPA in northern Taiwan, and found that concentrations of PM2.5 are higher in downtown of Taipei City, including stations of Datong, Zhongshan, Zhongzheng; in addition, the simulation performance were better during Summer and Autumn. The sources and formation mechanism of PM2.5 are complex, due to limited information of mobile sources, the simulation of spatial of concentration PM2.5 exist uncertainty. This study identified positive correlations between PM2.5 concentration and studied health outcomes in 6 districts in Taipei City, 19 districts in New Taipei City, and 12 districts in Taoyuan. The findings aforementioned were different from the negative association identified from simple linear models indicating the regional variation of risk in association with PM2.5 concentration that needs further study.
參考文獻
延伸閱讀
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