Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-6663-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-6663-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jul 2025
Research article |
| 02 Jul 2025
| 02 Jul 2025
Evaluating spatiotemporal variations and exposure risk of ground-level ozone concentrations across China from 2000 to 2020 using high-resolution satellite-derived data
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California 90095, United States
Jingru Cao
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Pablo E. Saide
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California 90095, United States
Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095, United States
Tong Ye
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Weihang Wang
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Ming Zhang
CORRESPONDING AUTHOR
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Jiejun Huang
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
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Using aircraft-based measurements off the Atlantic coast of Africa, we found the springtime smoke plume was strongly correlated with the amount of water vapor in the atmosphere (more smoke indicated more humidity). We see the same general feature in satellite-assimilated and free-running models. Our analysis suggests this relationship is not caused by the burning but originates due to coincident continental meteorology plus fires. This air is transported over the ocean without further mixing.
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Short summary
We analyzed ground-level ozone variations and exposure hotspots across China (2000–2020) at multiple scales using daily 1 km ozone data derived from satellite-sourced land-surface temperature data via a machine-learning hindcasting model. The dataset was validated using cross-validation and external measurements. A non-monotonic trend with regional and seasonal variations emerged, with turning points around 2008 and 2015. Ozone levels >100 μg m‾³ shifted from June to May, while levels >160 μg m‾³ expanded in the North China Plain.
We analyzed ground-level ozone variations and exposure hotspots across China (2000–2020) at...
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