https://scholars.lib.ntu.edu.tw/handle/123456789/640251
標題: | A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan | 作者: | Asri, Aji Kusumaning Lee, Hsiao-Yun Chen, Yu-Ling Wong, Pei-Yi Hsu, Chin-Yu PAU-CHUNG CHEN Lung, Shih-Chun Candice Chen, Yu-Cheng Wu, Chih-Da |
關鍵字: | Diurnal concentrations; Ensemble learning; Hybrid kriging-LUR; Machine-learning algorithms; NO(x) | 公開日期: | 15-三月-2024 | 出版社: | Elsevier B.V. | 卷: | 916 | 來源出版物: | The Science of the Total Environment | 摘要: | Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/640251 | ISSN: | 00489697 | DOI: | 10.1016/j.scitotenv.2024.170209 |
顯示於: | 環境與職業健康科學研究所 |
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