利用地理加權迴歸進行台北都會區二氧化氮之模擬分析

Estimation of NO_2 Variability in Taipei Metropolis Using Geographically Weighted Regression

10.6574/JPRS.202003_25(1).0001

翁佩詒(Pei-Yi Wong)；吳治達(Chih-Da Wu)；蘇慧貞(Huey-Jen Su)

空氣污染 ； 二氧化氮 ； 土地利用 ； 地理加權迴歸 ； Air pollution ； Nitrogen Dioxide (NO2) ； Land-use ； Geographically Weighted Regression (GWR)

航測及遙測學刊

25卷1期（2020 / 03 / 01）

1 - 10

繁體中文

二氧化氮（NO_2）為都會區最重要的空氣污染物之一。本研究以台北都會區為研究區，結合環境保護署18個監測站於2000年至2013年月平均空污濃度監測資料、以及土地利用的GIS資料，利用地理加權迴歸（Geographically Weighted Regression, GWR）推估NO_2的時空變異。結果指出，研究期間台北都會區的NO_2濃度呈現逐漸下降的趨勢，由2000年的25.94ppb減少為2013年的21.48ppb；模型分析結果指出，道路與污染具正相關，森林、水體與污染則呈負相關；所建模型之R^2達0.89，具有高度之預測與解釋力。最後利用所建模型推估台北都會區NO_2濃度之空間變異，結果顯示，污染物濃度較高的地區主要集中在台北市以及新北市人口稠密、道路交通發達之地區。

Nitrogen Dioxide (NO_2) is one of the major air pollutants in urban area. In this study, NO_2 concentration observations during 2000 to 2013 were obtained from 18 EPA monitoring stations. Geographically Weighted Regression (GWR) coupled with GIS land-use data was then applied to assess the spatial-temporal variability of NO_2 in Taipei metropolis. The results showed that, a slightly decreasing trend was found in the pollutant level during the studied fourteen years. For example, the averaged NO_2 concentration level was 25.49ppb in 2000 but decreased to 21.48ppb in 2013. Several land-use related variables were selected as important predictors in the developed GWR model. Among them, roads were positively corelated to pollutant levels. Forests and waterbodies showed a negative association. Moreover, the resultant model had a highly explanatory power with the model R^2 of 0.89. Finally, NO_2 variability was illustrated using the developed model. Higher pollutant levels were clustered in the densely populated areas with heavy traffic.

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