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：What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas? 中文：苯濃度在德州休士頓哈特曼公園社區周圍易受影響地區的時空分佈模式是什麼？ |
| 期刊名稱 | Journal of Hazardous Materials |
| 發表年份,卷數,起迄頁數 | 2024, 474: 134666 |
| 作者 | Asri, Aji Kusumaning; Newman, Galen D.; Tao, Zhihan; Zhu, Rui; Chen, Hsiu-Ling; Lung, Shih-Chun Candice; Wu, Chih-Da(吳治達)* |
| DOI | 10.1016/j.jhazmat.2024.134666 |
| 中文摘要 | 位於美國德州休士頓的哈特曼公園社區處於高度污染區，對以西班牙裔和低收入居民為主的當地居民構成了重大風險。社區周圍密集的工業設施進一步加劇了健康與安全的危害，並加重了環境與社會不平等的問題。這些條件凸顯出採取環境措施、特別是調查環境空氣品質的緊迫需求。本研究利用機器學習為基礎的方法，對哈特曼公園最常報導的污染物之一苯進行了估算。苯的數據來自社區住宅區，並透過五種機器學習算法（即XGBR、GBR、LGBMR、CBR、RFR）組合成的新開發集成學習模型進行分析。研究進行了模型穩健性評估、過擬合測試、10倍交叉驗證、內部和分層驗證。我們發現集成模型能夠解釋約98.7%的苯空間變異性（調整R2=0.987）。通過嚴格的驗證，確認了模型表現的穩定性。研究還確定了幾個對苯含量有貢獻的預測因子，包括溫度、開發強度區域、石油儲罐洩漏以及交通相關因素。通過分析空間模式，我們發現在工業區附近及住宅區內均存在高濃度的苯擴散。整體而言，我們的研究區域暴露於高苯濃度，亟需相關機構的額外關注。 |
| 英文摘要 | The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities. |
| 發表成果與本中心研究主題相關性 | 本研究合作單位德州農工大學為本校中興大學之重點國際合作學校。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，與本中心高度相關。 |
