Articles | Volume 19, issue 19
https://doi.org/10.5194/acp-19-12413-2019
© Author(s) 2019. 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-19-12413-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2019
Research article |
| 08 Oct 2019
| 08 Oct 2019
Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau
Dongren Liu
Department of Environmental Science and Engineering, Sichuan
University, Chengdu 610065, China
Baofeng Di
Department of Environmental Science and Engineering, Sichuan
University, Chengdu 610065, China
Institute for Disaster Management and Reconstruction, Sichuan
University, Chengdu 610200, China
Yuzhou Luo
Department of Land, Air, and Water Resources, University of
California, Davis, CA 95616, USA
Xunfei Deng
Institute of Digital Agriculture, Zhejiang Academy of Agricultural
Sciences, Hangzhou 310021, China
Hanyue Zhang
Department of Environmental Science and Engineering, Sichuan
University, Chengdu 610065, China
Fumo Yang
Department of Environmental Science and Engineering, Sichuan
University, Chengdu 610065, China
National Engineering Research Center for Flue Gas Desulfurization,
Chengdu 610065, China
Michael L. Grieneisen
Department of Land, Air, and Water Resources, University of
California, Davis, CA 95616, USA
Department of Environmental Science and Engineering, Sichuan
University, Chengdu 610065, China
National Engineering Research Center for Flue Gas Desulfurization,
Chengdu 610065, China
Sino-German Centre for Water and Health Research, Sichuan University,
Chengdu 610065, China
Medical Big Data Center, Sichuan University, Chengdu 610041, China
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33 citations as recorded by crossref.
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- Global spatiotemporal estimation of daily high-resolution surface carbon monoxide concentrations using Deep Forest Y. Wang et al. https://doi.org/10.1016/j.jclepro.2022.131500
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- Improving the quantification of peak concentrations for air quality sensors via data weighting C. Frischmon et al. https://doi.org/10.5194/amt-18-3147-2025
- Third trimester as the susceptibility window for maternal PM2.5 exposure and preterm birth: A nationwide surveillance-based association study in China Z. Qiu et al. https://doi.org/10.1016/j.scitotenv.2023.163274
- Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022 Y. Xie et al. https://doi.org/10.1016/j.atmosenv.2025.121163
- Wastewater, air quality, and environmental justice in the Southern West Bank: an integrated analysis using remote sensing, geospatial analysis, and terrain modeling (2019–2024) N. Nassar et al. https://doi.org/10.1007/s41207-026-01143-7
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Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
The spatiotemporal distributions of daily ground-level CO concentrations across China during 2013–2016 are derived by fusing the data from remote sensing and ground monitoring. The population–weighted CO was predicted to be 0.99 ± 0.30 mg m−3 and showed a decreasing trend of −0.021 ± 0.004 mg m−3 per year. The CO pollution was the most severe in the North China Plain. The hotspots in the Tibetan Plateau overlooked by the remote sensing were depicted by the data-fusion approach.
The spatiotemporal distributions of daily ground-level CO concentrations across China during...
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