| 摘要註: |
空氣中細懸浮微粒(Fine Particulate Matters，粒徑小於2.5um，簡稱PM2.5)對於環境及人體健康的影響甚鉅，各國及世界衛生組織(WHO)均逐步訂定PM2.5空氣品質標準或管制標準。台灣環保署已於2012年5月將PM2.5納入空氣品質標準，訂定24小時平均值35ug/m3、年平均值15ug/m3之環境目標。因此，對掌握全台PM2.5在時空上之分佈並進而建立可靠的預測模型成為當前重要之工作。本研究採用空氣品質監測站2007~2011年連續監測之結果，分別針對台灣北部、中部、南部等三個地區選取5種不同背景測站(一般、交通、國家公園、工業、背景)之資料，共計16個測站，考量包括大氣溫度、雨量、濕度、風速、一氧化氮、二氧化氮、氮氧化物、臭氧、二氧化硫、懸浮微粒及細懸浮微粒等氣象因子與污染物類型，分析全台PM2.5濃度分佈之時空之差異，以主成份分析結合多元迴歸分析及時間序列分析，建立各測站之PM2.5預測模型。分析結果顯示：主成份分析結合多元迴歸分析擁有良好預測效果，而全台各測站之主要相關因子不盡相同，北部空品區前二主要因子為NOx、PM10，中南部之空品區主要因子皆顯示空氣污染物與O3間之關係，顯示中南部之空氣品質狀況與光化生成反應影響較大。而在時間序列模型建立方面，南部測站之模型長度仍高於北、中部，顯示受污染情形仍高於其他地方，整體測站在前三期大致符合實際狀況，後期預測結果趨近平均值，研判PM2.5具有異質性之影響，以致模型無法完整預測。最後在集群分析不同條件分群結果中，陽明測站在全台16測站中具有一定之獨特性與代表性，應與其地理環境之特殊性有關，而使用K-Means分群法將全台測站分成3群，分群結果可看出不同集群間之差異性，在氣象條件分群中大致分為背景公園站為一群，其餘則以其他都市測站為主，而在污染物分群中可看出以北部測站為主之第二集群，污染物主要以氮氧化物為主，而高屏測站為主之第三集群，則以懸浮微粒及硫化物為主，可明顯看出分群之差異性。 Due to the impact of fine particulate matters (PM2.5) on health and environment, Taiwan is scheduled to make up the control standards in 2012. The standard was 24-hour average 35ug/m3 and the annual average 15ug/m3. In this research, we used the air quality station's monitoring data from 2007~2011. Select the three regions of northern, central and southern Taiwan include 5 different backgrounds station (general, traffic, national parks, industrial, background) total of 16 stations. The monitoring item contains Weather factors (temp, rain, humidity, wind speed) and pollutants factors (NO, NO2, NOx, CO, O3, SO2, PM10, PM2.5, CH4, THC, NMHC ). We plan to focus on the problems about understand the path of distribution of the fine particulate matters, its origin and its impacts to monitoring and control issues. To establish PM2.5 prediction models of each station. The method includes Principal Component Analysis (PCA), Multiple Regression Analysis, Time Series Analysis and Cluster Analysis. The results indicate that PCA combined with Multivariate Regression Analysis to predict with good results. The first principal component (PC1) shows the concentration of air pollutants are decreased with wind speed and temperature increased in northern and southern Taiwan, respectively. The second principal component (PC2) shows most of stations presents the high correlation between O3 and air pollutants. It appears the significant influence for air quality by photochemical reaction. In the Time Series Analysis, the model length is southern more than northern Taiwan. Show that the southern was still contaminated than other places. In the prediction part, approximately equal with the actual situation of three lags. After three lags, due to the effects of heterogeneity, the predicted value approaching the average mean. In the Cluster Analysis, we used different conditions to understand the cluster of station. The result shows that Yangming station has some of unique and representative because of geographical environment. And we used K-Means Cluster divide the station into three clusters of stations in order to understand the differences between different clusters. |