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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30 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. 10.1016/j.jclepro.2022.131500
- Semen quality and windows of susceptibility: A case study during COVID-19 outbreak in China T. Yang et al. 10.1016/j.envres.2021.111085
- Accelerated toluene degradation over forests around megacities in southern China Q. Li et al. 10.1016/j.ecoenv.2021.113126
- Adoption of cleaner technologies and reduction in fire events in the hotspots lead to global decline in carbon monoxide A. Joshi et al. 10.1016/j.chemosphere.2023.139259
- 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. 10.1016/j.scitotenv.2023.163274
- Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022 Y. Xie et al. 10.1016/j.atmosenv.2025.121163
- Association between maternal exposure to gaseous pollutants and atrial septal defect in China: A nationwide population-based study F. Yan et al. 10.1016/j.envres.2021.111472
- Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
- Substantially underestimated global health risks of current ozone pollution Y. Wang et al. 10.1038/s41467-024-55450-0
- Satellite-based estimates of high-resolution CO concentrations at ground level in the Yangtze River Economic Belt of China J. Dong et al. 10.1016/j.atmosenv.2023.120018
- Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations B. Chen et al. 10.1038/s41612-023-00559-0
- Acute effects of short-term exposure to ambient air pollution on reproductive hormones in young males of the MARHCS study in China F. Wang et al. 10.1016/j.scitotenv.2021.145691
- Exploring non-linear and spatially non-stationary relationships between commuting burden and built environment correlates Z. Tong et al. 10.1016/j.jtrangeo.2022.103413
- A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning Q. Yang et al. 10.1016/j.scitotenv.2022.159542
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- A real-time assessment of hazardous atmospheric pollutants across cities in China and India S. Rahaman et al. 10.1016/j.jhazmat.2024.135711
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- Joint effects of green space and air pollutant exposure on preterm birth: evidence from a nationwide study in China T. Mi et al. 10.1007/s11356-024-33561-x
- Satellite-Based Reconstruction of Atmospheric CO2 Concentration over China Using a Hybrid CNN and Spatiotemporal Kriging Model Y. Hua et al. 10.3390/rs16132433
- A five-year Study Using Sentinel-5P Data Observing Seasonal Dynamics and Long-term Trends of Atmospheric Pollutants H. Mohamed et al. 10.26833/ijeg.1587122
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. 10.1029/2023JD038939
- Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland T. Karppinen et al. 10.1016/j.rsase.2023.101120
Latest update: 04 Apr 2025
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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