非甲烷碳氫化合物（NMHC）在對流層臭氧形成及二次空氣污染物生成中扮演關鍵角色。NMHC與氮氧化物（NOₓ）進行複雜的光化學反應，尤其在太陽輻射作用下，顯著促進地面臭氧（O₃）累積。這些相互作用不僅降低空氣品質，還對呼吸系統與心血管系統造成嚴重健康風險。在台灣，由於快速工業化與都市化加劇了人為排放，NMHC及其共污染物如二氧化氮（NO₂）受到密切監測，因其對臭氧事件具有協同影響。然而，儘管現有空氣品質監測網絡完善，NMHC濃度的詳細時空預測仍相對有限。

本研究旨在利用基於集成機器學習的算法，估算台灣NMHC濃度的時空變化。研究應用了五種機器學習模型進行比較：Light Gradient Boosting Machine Regressor（LGBMR）、Gradient Boosting Regressor（GBR）、CatBoost Regressor（CBR）、Random Forest Regressor（RFR），以及將表現最佳模型組合的堆疊集成模型。預測變數包括氣象資料（如溫度、風速）、空氣品質指標（如NO₂、PM₁₀）及GIS衍生空間特徵（如道路鄰近度、土地利用分類、距海岸距離），這些變數依據其環境相關性與對NMHC變異的統計貢獻選取。
模型驗證採用多種策略，包括10倍交叉驗證、空間與時間留出驗證，以及以2019年資料進行外部驗證。在所有模型中，堆疊集成模型在各種驗證方法中均達到最高整體準確度，訓練集R²為0.89，測試集及外部驗證均超過0.60，顯示其模型具高度穩健性。變數重要性分析顯示，NO₂為預測NMHC濃度的最具影響力變數，其次為溫度、NOx及當地道路密度，反映交通排放與氣象條件的綜合作用。平均預測NMHC濃度約為0.09 ppm，且在高度都市化與工業區域，如高雄與台北，持續顯現空間熱點。
本研究證明，將集成機器學習模型與空間資料整合，可提升空氣污染預測的準確性，並有效識別排放熱點。研究成果可支援環境決策，包括針對NMHC的減排策略與臭氧緩解行動。此外，該框架亦可擴展至其他污染物，或應用於具類似排放特徵與監測能力的區域。

Non-methane hydrocarbons (NMHC) play a critical role in tropospheric ozone formation and secondary air pollutant generation. Through complex photochemical reactions with nitrogen oxides (NOₓ), particularly under the influence of solar radiation, NMHC contribute significantly to the buildup of ground-level ozone (O₃). These interactions not only degrade air quality but also pose severe health risks, particularly affecting respiratory and cardiovascular systems. In Taiwan, where rapid industrialization and urbanization have intensified anthropogenic emissions, NMHC and their co-pollutants such as NO₂ are closely monitored due to their combined impact on ozone episodes. Despite the availability of air quality monitoring networks, detailed spatiotemporal predictions of NMHC concentrations remain limited.
This study aims to estimate the spatial and temporal variation of NMHC concentrations across Taiwan using ensemble-based machine learning algorithms. Five machine learning models were applied and compared: Light Gradient Boosting Machine Regressor (LGBMR), Gradient Boosting Regressor (GBR), CatBoost Regressor (CBR), Random Forest Regressor (RFR), and a stacked ensemble machine learning model combining the top-performing algorithms. Predictor variables included meteorological data (e.g., temperature, wind speed), air quality indicators (e.g., NO₂, PM₁₀), and GIS-derived spatial features (e.g., road proximity, land use classification, distance to coastlines). These variables were selected based on their environmental relevance and statistical contribution to NMHC variability. Model validation involved multiple strategies, including 10-fold cross-validation, spatial and temporal holdouts, and external validation using 2019 data. Among all models, the stacked ensemble model achieved the highest overall accuracy across validation approaches, with an R² of 0.89 on the training set and over 0.60 in both testing and external validation, highlighting its robustness. Analysis of variable importance indicated that NO₂ was the most influential variable in predicting NMHC concentrations, followed by temperature, NOx, and local road density reflecting the combined influence of traffic emissions and meteorological conditions. The average predicted NMHC concentration was approximately 0.09 ppm, with consistent spatial hotspots identified in highly urbanized and industrial regions such as Kaohsiung and Taipei.
This study demonstrates that integrating ensemble machine learning models with spatial datasets enhances the accuracy of air pollution prediction and enables better identification of emission hotspots. The modeling results can support environmental decision-making, including targeted NMHC reduction strategies and ozone mitigation efforts. Furthermore, this framework may be extended to other pollutants or applied in regions with similar emission profiles and monitoring capabilities.

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