Regression Models For The Traffic-RelatedAir Pollutants In Taipei Metropolitan Area Using Land Use Regression And Data From Air Monitoring Stations.

• Main Authors: Lee, Chung-Hsien, 李忠憲
• Other Authors: Lai, Ching-Huang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/63825162883511519318

碩士 === 國防醫學院 === 公共衛生學研究所 === 100 === There are more and more research demonstration that air pollutant has bad effects on the human body and health. The result from monitoring stations is unable to represent the pollutant condition in all places, therefore, establishment of pollutant models to predict the pollutant concentration of unknown area is the tendency also necessary. Land Use Regression (LUR) contained the data from geography infor-mation system (GIS) had already succeed to predict the spatial variation of the air pollutant concentration in the past research, and demonstrated that its results are better than other models. Base on the previous research in European and American, this research establishes the simple linear regression models of the transportation related pollutant include the carbon monoxide (CO), the nitrogen oxide (NOx), the ozone (O3), and the suspended particulate matter (PM10) in Taipei metropolitan area using the Taiwan Environmental Protection Agency (EPA) air quality monitoring station information from year 2006 to 2009, and using data from Taipei City Environmental Protection Agency monitoring station for verification, to investigate the models are suitable and impacts. In lacks of the traffic flow information, this findings discovered that CO and the NOx models (r2 = 0.76, 0.67) predictive ability are better than O3 and the PM10 (r2 = 0.27, 0.19) and match the results of past studies, and we might know that models creation using the annual mean to compare in the monthly mean effect is better, but used the maximum value establishment models not to achieve the expectation effect.