Articles | Volume 23, issue 2
https://doi.org/10.5194/acp-23-1511-2023
© Author(s) 2023. 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-23-1511-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2023
Research article |
| 26 Jan 2023
| 26 Jan 2023
Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations
Jing Wei
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, USA
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
Pawan Gupta
STI, Universities Space Research Association (USRA), Huntsville, AL, USA
NASA Marshall Space Flight Center, Huntsville, AL, USA
Maureen Cribb
Department of Atmospheric and Oceanic Science, Earth System Science
Interdisciplinary Center, University of Maryland, College Park, MD, USA
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63 citations as recorded by crossref.
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- Extreme Temperature Events, Fine Particulate Matter, and Myocardial Infarction Mortality R. Xu et al. 10.1161/CIRCULATIONAHA.122.063504
- Exposure to ambient air pollution and metabolic dysfunction-associated fatty liver disease: Findings from over 2.7 million adults in Northwestern China W. Ji et al. 10.1016/j.ecoenv.2024.116109
- Dry deposition effect of urban green spaces on ambient particulate matter pollution in China J. Yao et al. 10.1016/j.scitotenv.2023.165830
Latest update: 25 Apr 2024
Short summary
This study estimated the daily seamless 10 km ambient gaseous pollutants (NO2, SO2, and CO) across China using machine learning with extensive input variables measured on monitors, satellites, and models. Our dataset yields a high data quality via cross-validation at varying spatiotemporal scales and outperforms most previous related studies, making it most helpful to future (especially short-term) air pollution and environmental health-related studies.
This study estimated the daily seamless 10 km ambient gaseous pollutants (NO2, SO2, and CO)...
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