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A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.
Asri, Aji Kusumaning; Lee, Hsiao-Yun; Chen, Yu-Ling; Wong, Pei-Yi; Hsu, Chin-Yu; Chen, Pau-Chung; Lung, Shih-Chun Candice; Chen, Yu-Cheng; Wu, Chih-Da.
Affiliation
  • Asri AK; Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan. Electronic address: akusumaning@gmail.com.
  • Lee HY; Department of Leisure Industry and Health Promotion, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan. Electronic address: hsiaoyun07@ntunhs.edu.tw.
  • Chen YL; Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan. Electronic address: f64061070@gs.ncku.edu.tw.
  • Wong PY; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan. Electronic address: aa6624tw@gmail.com.
  • Hsu CY; Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, Taiwan. Electronic address: gracecyhsu@mail.mcut.edu.tw.
  • Chen PC; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei, Taiwan; Department of Environmental and Occupational Medicine, National
  • Lung SC; Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan. Electronic address: sclung@rcec.sinica.e
  • Chen YC; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan. Electronic address: yucheng@nhri.edu.tw.
  • Wu CD; Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Ta
Sci Total Environ ; 916: 170209, 2024 Mar 15.
Article de En | MEDLINE | ID: mdl-38278267
ABSTRACT
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.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Polluants atmosphériques / Pollution de l'air Type d'étude: Prognostic_studies Limites: Humans Pays/Région comme sujet: Asia Langue: En Journal: Sci Total Environ Année: 2024 Type de document: Article

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Polluants atmosphériques / Pollution de l'air Type d'étude: Prognostic_studies Limites: Humans Pays/Région comme sujet: Asia Langue: En Journal: Sci Total Environ Année: 2024 Type de document: Article