生態農業：氣候變遷下森林碳匯與生態經濟評估【國立成功大學測量及空間資訊學系/吳治達教授】
| 論文篇名 | 英文：Estimating morning and dusk commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology 中文：使用地理空間人工智能（Geo-AI）技術和高空間-時間解析度的集成混合空間模型，估算台灣早晨和黃昏通勤時段的臭氧（O₃）濃度 |
| 期刊名稱 | Journal of Environmental Management |
| 發表年份,卷數,起迄頁數 | 2024, 351: 119725 |
| 作者 | Hsu, Chin-Yu; Lee, Ruei-Qin; Wong, Pei-Yi; Lung, Shih-Chun Candice; Chen, Yu-Cheng; Chen, Pau-Chung; Adamkiewicz, Gary; Wu, Chih-Da(吳治達)* |
| DOI | 10.1016/j.jenvman.2023.119725 |
| 中文摘要 | 地面臭氧（O₃）濃度升高對健康有害。儘管以往研究主要集中在日均值和日間模式上，但考慮日常通勤過程中的空氣污染影響同樣重要，因為這對整體暴露有顯著貢獻。本研究首次使用集成混合空間模型（EMSM），該模型整合了多種機器學習算法和通過Shapley Additive Explanations（SHAP）值選擇的預測變量，用於預測台灣全島臭氧濃度的空間-時間波動。我們利用地理空間人工智能（Geo-AI），結合克里金插值、土地利用回歸（LUR）、機器學習（隨機森林（RF）、分類提升（CatBoost）、梯度提升（GBM）、極端梯度提升（XGBoost）和輕量級梯度提升（LightGBM）），以及集成學習技術，開發了早晚通勤時段的集成混合空間模型（EMSMs）。這些EMSMs用於估算O₃水平的長期空間-時間變化，考慮了現場測量、氣象因素、地理預測因子、社會和季節性影響，涵蓋了26年的數據。與傳統的LUR方法相比，EMSMs在兩個通勤時段的性能提高了58%，解釋力高，調整後的R²為0.91。內部和外部驗證程序以及對O₃濃度在上百分位範圍（1%、5%、10%、15%、20%和25%）及其他條件（包括降雨、無降雨、工作日、週末、節日和非節日）的驗證表明，模型穩定且無過擬合問題。生成的估算地圖用於檢查COVID-19限制措施實施前後的O₃水平變化。這些結果提供了高空間-時間解析度（每日和50米×50米網格）下通勤時段O₃水平的準確變化，對污染控制和流行病學研究具有支持作用。 |
| 英文摘要 | Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it’s crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies. |
| 發表成果與本中心研究主題相關性 | 本研究探討之主題著眼在地理人工智慧於環境汙染與永續環境管理之應用，模型中亦探討綠覆率對臭氧汙染之影響，與本中心高度相關。 |
