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High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.
- Source :
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Environmental research [Environ Res] 2024 Jun 15; Vol. 251 (Pt 1), pp. 118609. Date of Electronic Publication: 2024 Mar 03. - Publication Year :
- 2024
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Abstract
- Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of ground observations, there is a pressing need for high-precision models to simulate ground-level ozone to assess surface ozone pollution. In this study, we have compared several widely utilized ensemble learning and deep learning methods for ground-level ozone simulation. Furthermore, we have thoroughly contrasted the temporal and spatial generalization performances of the ensemble learning and deep learning models. The 3-Dimensional Convolutional Neural Network (3-D CNN) model has emerged as the optimal choice for evaluating the daily maximum 8-h average ozone in Yunnan Province. The model has good performance: a spatial resolution of 0.05° × 0.05° and strong predictive power, as indicated by a Coefficient of Determination (R <superscript>2</superscript> ) of 0.83 and a Root Mean Square Error (RMSE) of 12.54 μg/m³ in sample-based 5-fold cross-validation (CV). In the final stage of our study, we applied the 3-D CNN model to generate a comprehensive daily maximum 8-h average ozone dataset for Yunnan Province for the year 2021. This application has furnished us with a crucial high-resolution and highly accurate dataset for further in-depth studies on the issue of ozone pollution in Yunnan Province.<br />Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Rui Liu reports partial materials was provided by the China University of Mining and Technology.Rui Liu reports writing assistance was provided by LetPub (www.letpub.com).<br /> (Copyright © 2024 Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1096-0953
- Volume :
- 251
- Issue :
- Pt 1
- Database :
- MEDLINE
- Journal :
- Environmental research
- Publication Type :
- Academic Journal
- Accession number :
- 38442812
- Full Text :
- https://doi.org/10.1016/j.envres.2024.118609