苯對健康的危害已是眾所皆知，除了短期攝入高濃度的苯會出現暈眩、嘔吐、高度知覺喪失，長期吸入則會誘發血液相關疾病、染色體畸變、白血病及死亡。依照衛生福利部統計處資料顯示，攝入苯最主要誘發的白血病人數逐年上升，因此了解苯濃度的分布對於國人的健康評估具有急迫性及研究必要性。本研究旨在結合地理資訊系統、遙測影像、土地利用迴歸及機械學習等技術，並考量與苯濃度相關之因子所建立之地標資料庫以更細部探討與其之間的關係。在台灣本島方面，基於2003年至2019年，長達17年的月平均空污監測數據；林園臨海則從有監測數據以來，自2015年5月至2019年底，並利用線性的逐步迴歸法(Stepwise Regression)以審慎篩選出有效預測苯濃度的相關因子，再以線性的土地利用迴歸(Land-use Regression, LUR)、非線性機器學習之隨機森林(Random Forest, RF)及極限梯度提升(eXtreme Gradient Boosting, XGBoost)演算法進行模型擬合，以發展符合在地特性之苯污染時空推估模型；同時，為確保模型具有一定程度的穩定性及預測準確性，本研究總共執行九項模型驗證，並在驗證結果中體現模型的可信度。結果顯示，在林園臨海特殊性工業區所建置之模型方面，以Hybrid-LUR-XGBoost的模型解釋能力為最佳，Adjusted R2為0.73，RMSE為0.78 ppbv。台灣本島模型方面，因有些月份測站數量稀少故未能使用以leave-one-out Kriging所建置之Hybrid-LUR模型。其最佳模型表現為LUR-XGBoost，Adjusted R2為0.82，RMSE為0.77 ppbv，並且在後續的模型驗證中體現了穩定性，以及在模型的時間以及空間的外推能力上，都表現出不錯的成果。在推估濃度方面，以工業區、人口稠密區苯濃度較高。

Benzene is one of the VOCs (Volatile Organic Compounds) that might affect human respiratory and hematopoietic system. According to World Health Organization (WHO) in 2003 for claiming the hazard of benzene, dizziness, headache, and high levels unconsciousness might be associated with short-term intake. For the long-term exposures, the risk of contracting aplastic anemia might be increased. Few studies focused on benzene concentration estimation in the last decade, but the hazards of benzene have received attention greatly. This highlights the importance in developing a methodology for identifying the spatial-temporal variations of benzene, and further to assess people’s exposure level to benzene. Among these spatial modelling approaches, land-use regression (LUR) is one of the approaches which is widely applied in spatial modelling for various types of air pollutant. By utilizing a stepwise variable selection procedure, LUR could identify important land-use/land cover predictors and form a multiple linear regression model to predict the spatial variations of air pollution. Recently, the development of machine learning algorithms provides an insight to assess the non-linear relationship between air pollution and spatial predictors, which might be helpful in improving the model prediction accuracy. The aim of this study was to apply LUR coupled with machine learning algorithms for accurately estimating the s spatial-temporal variations of ambient benzene in the main island of Taiwan and industrial parks in Kaohsiung city with a monthly resolution.
Monthly benzene observations from May, 2015 to December, 2019, and January, 2003 to December, 2019 were collected for the Kaohsiung Linyuan-Linhai industrial parks and the main island of Taiwan, respectively. A stepwise variable selection procedure was used to identify the important predictor variable based on the statistical criteria. Several spatial model approach or algorithms including Land-use Regression (LUR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were then applied to fit the models using these selected predictor variables. Six internal data validations and three external data verifications were employed to confirm the stability and reliability of the developed models. The results showed LUR-XGBoost approach obtained the best prediction performance for the Kaohsiung Linyuan-Linhai industrial parks with adjusted R2 of 0.82 and RMSE of 0.77 ppbv. On the other hand, Hybrid-LUR-XGBoost outperformed the other models for the main island of Taiwan and the obtained adjusted R2 and RMSE was 0.73 and 0.78 ppbv, respectively. Similar findings were observed from the model validations and verifications tests confirmed the robustness of the developed models. Finally, densely populated area and nearby industries were hotspots of benzene pollutant.

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