細懸浮微粒（PM2.5）所造成的空氣汙染已經成為各國政府主要的環境問題之一。因此我們有必要監測與預測空氣質量，以便環境管理及控制。先前研究中結合多種因子訓練土地利用回歸模型，以台北市資料為例預估PM2.5的濃度，包含化學粒子、氣象資訊、環境綠化和地標等，並凸顯亞洲都市的典型特徵。近期更是有許多研究關注改善尤其是PM2.5空氣汙染的預估。除了於GIS資料庫中常用的預估法，本研究透過Landsat-8影像中的地面資訊、水體、森林及建成區作為PM2.5濃度預測之模型建構。因此，本研究除使用GIS資料庫中的預測器之外，結合了光學衛星影像的分類器預估PM2.5的濃度。實驗使用2013至2018年間17個EPA空氣品質監測站之PM2.5年度平均值，作為實際資料並訓練土地利用回歸模型，並使用了10折交叉驗證評估實驗結果。定量分析使用決定係數(R2)與均方根誤差量化模型能力表現。

Fine particulate matter (PM2.5) is an air pollutant that has been becoming one of the major environmental issues in national governments. Air quality monitoring and prediction are thus necessary for management and control. In previous studies, a land-use regression (LUR) model with several factors such as chemical particles, meteorological information, greenness environments, and landmarks combined with interpolation techniques is used to predict PM2.5 concentrations using data from Taipei metropolis, which exhibits typical Asian city characteristics. Recently, a lot of attention was paid to the improvement of methods that are used to predict air quality, especially PM2.5. This study proposed adopting ground information, including water body, forest, and built-up area, from Landsat-8 images as predictors in the modeling of PM2.5 concentration, in addition to the commonly-used predictors from the GIS database. Thus, this study used not only the predictors from the GIS database but the predictors from the classification of optical satellite images to model the PM2.5 concentrations. For the in-situ samples used in the model training, an annual average of PM2.5 concentrations from 2013-2018 of 17 air-quality monitoring stations established by EPA Ire used, and LUR is used as the regression method. In order to obtain the results, the 10-fold cross-validation methodology is used for the proposed method. Quantitative accuracy assessment is done to demonstrate the capability of the proposed model in terms of determination coefficient (R2) and root means square error (RMSE).

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