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A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.
Babaan, Jennieveive; Hsu, Fang-Tzu; Wong, Pei-Yi; Chen, Pau-Chung; Guo, Yue-Leon; Lung, Shih-Chun Candice; Chen, Yu-Cheng; Wu, Chih-Da.
  • Babaan J; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
  • Hsu FT; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
  • Wong PY; Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan.
  • 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
  • Guo YL; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • 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.
  • Chen YC; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
  • Wu CD; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan. Electronic address: chidawu@mail.ncku.edu.tw.
J Hazard Mater ; 446: 130749, 2023 03 15.
Article in English | MEDLINE | ID: covidwho-2165552
ABSTRACT
High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ozone / Air Pollutants / Air Pollution / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: J Hazard Mater Journal subject: Environmental Health Year: 2023 Document Type: Article Affiliation country: J.jhazmat.2023.130749

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ozone / Air Pollutants / Air Pollution / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: J Hazard Mater Journal subject: Environmental Health Year: 2023 Document Type: Article Affiliation country: J.jhazmat.2023.130749