二氧化氮(Nitrogen dioxide, NO2)的生成與人為產業活動及交通移動行為密切。其反應活潑，涉及複雜光化學反應會衍生出有害二次汙染物。除此之外，NO2會對人體健康造成急性與長期呼吸道與心血管系統相關危害。隨著對空污議題的重視，政府更修法提高室外NO2濃度的管控標準。現行全台範圍高品質NO2濃度監測主要來自環保署監測站，然而有限測站的觀測難以有效描述NO2在時空上連續的分布與變化。除此之外，每日通勤過程是人們暴露於室外汙染的主要時段。在上午與下午通勤時段的尖峰車潮與龐大污染排放量，使得室外NO2暴露量在通勤期間達到每日高峰。然而過往模擬大範圍NO2研究僅考慮晝夜差異，未能良好考慮人為戶外活動的主要時段。綜上所述，精準地針對通勤尖峰時段，評估NO2濃度在大範圍濃度的時空分布是當今迫切的課題。近年科技的演進使得機器學習(Machine Learning, ML)在空汙推估領域已廣泛運用且展現良好預測性能；又單一模型在預測準確度上有限，因此本研究基於集成學習概念建立出集成混合學習模型(Ensemble Spatial Mixed Model, EMSM)，其結合空間資訊技術並擷取空間內插、機器學習等方法的特長。本研究旨在運用集成混合學習模型推估臺灣本島NO2濃度在上午與下午通勤時段的時空變異與分布。
臺灣本島四面環海且境內地勢起伏明顯，人口高度集中於西半部都市，眾多汽機車與工業區排放源為人民健康帶來隱憂。本研究使用1994至2019年臺灣本島74個環保署測站NO2監測值，並計算為上午通勤(早上7:00-9:00)與下午通勤(下午16:00-18:00)時段平均值。本研究納入空氣汙染物、氣象、土地利用等8大類潛在影響變數以建立NO2濃度推估模型。考慮NO2的時空自相關特性，運用空間內插法產製NO2推估值作為預測變數。在變數篩選上，本研究採用統計迴歸與機器學習兩種策略：首先藉由逐步迴歸法篩選並建立兩種土地利用迴歸模型；針對機器學習模型，透過計算並排序預測變數的SHAP(SHapley Additive exPlanations)值進而辨識出重要變數，再分別以5種演算法建模並評估模型表現，包括隨機森林(Random Forest, RF)、梯度提升機(Gradient Boosting, GBM)、極限梯度提升機(eXtreme Gradient Boosting, XGBoost)、輕量級梯度提升機(Light Gradient Boosting, LightGBM)以及類別梯度提升(Categorical Boosting, CatBoost)。集成混合空間模型係由5種機器學習模型中預測能力良好且未有過擬合問題模型之預測值作為預測變數，經由堆疊泛化集成方式再次擬合而成。經過模型驗證比較各方法學模型推估性能，並選擇預測性能最佳模型產製NO2濃度時空分布圖。
研究成果顯示：集成混合空間模型在上午與下午時段模型Adjusted R2皆為0.90，其解釋能力分別較傳統土地利用迴歸高出22%與23%；在前5%極端高值資料仍有50%與66%的中等預測水準，較單一機器學習模型穩定。經由內部與外部資料驗證證實模型可靠穩健且無過擬合問題，在不同空間與時間尺度皆有良好預測表現，模型也具備適宜的外推應用性。從長期(1994至2019年)臺灣本島高空間解析度(50m)推估成果觀察上午與下午通勤時段NO2濃度的時空分布趨勢：空間上，西半部市區濃度較高、中央與東部山區濃度較低，在臺北市與高雄市區出現高濃度群聚特徵；時間上，NO2濃度在兩通勤時段相近，因混合層高度差異導致NO2在冬季較高、夏季較低。長期而言全台各地濃度呈顯著下降趨勢，也存在平日與周末的濃度差異。從2021年實施COVID-19全國三級警戒的情境中，也觀察到整體NO2濃度下降、空氣品質改善的狀況。本研究證明，集成混合空間具備空間內插、機器學習演算法與集成學習法各自的優點，能準確地推估臺灣通勤時段NO2的時空分布與變異狀況。本模型推估成果能運用於評估通勤尖峰時段的個人健康風險或通勤，提供公共衛生決策與交通管理政策擬定之參考。

Exposure to Nitrogen dioxide (NO2) could cause adverse health effects including respiratory and cardiovascular diseases. Elevated outdoor NO2 concentrations are emitted during daily rush hours when people are primarily exposed to outdoor NO2. However, limited studies focused on estimating NO2 during commuting periods at large spatial coverage. To overcome this challenge, this study applied an Ensemble mixed spatial model (EMSM) which integrated kriging interpolation, machine learning, and ensemble learning to better portray NO2 variations during commuting periods.
Long-term (1994-2019) NO2 observations from 74 Taiwan EPA stations were aggregated depending on the morning (7:00 am-9:00 am) and dusk (4:00 pm-6:00 pm) commuting periods. Eight types of potential predictors were collected. Two land use regression (LUR) models were constructed by the regression variable selection approach. Important variables of machine learning (ML) algorithms were ranked and identified by the SHAP (SHapley Additive exPlanations) value approach. By combining NO2 estimations from ML models, finalized NO2 estimates were predicted by the Ensemble mixed spatial model (EMSM). After model validation, long-term (1994-2019) daily NO2 distribution maps of Taiwan were produced by the EMSM model.
The results revealed that EMSM outperformed other models with Adjusted R2 of 0.90 for both commuting periods. Compared with the LUR model, model prediction capability improves by 22% and 23%. This study demonstrated that EMSM could combine the merits of spatial interpolation, machine learning, and ensemble learning. It could estimate the spatiotemporal variation of NO2 level with a 50 m resolution in the main island of Taiwan during commuting periods.

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