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
Viewed
Total article views: 2,287 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 26 Apr 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,516 | 716 | 55 | 2,287 | 339 | 47 | 63 |
- HTML: 1,516
- PDF: 716
- XML: 55
- Total: 2,287
- Supplement: 339
- BibTeX: 47
- EndNote: 63
Total article views: 1,800 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Oct 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,243 | 509 | 48 | 1,800 | 193 | 44 | 58 |
- HTML: 1,243
- PDF: 509
- XML: 48
- Total: 1,800
- Supplement: 193
- BibTeX: 44
- EndNote: 58
Total article views: 487 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 26 Apr 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 273 | 207 | 7 | 487 | 146 | 3 | 5 |
- HTML: 273
- PDF: 207
- XML: 7
- Total: 487
- Supplement: 146
- BibTeX: 3
- EndNote: 5
Viewed (geographical distribution)
Total article views: 2,287 (including HTML, PDF, and XML)
Thereof 2,167 with geography defined
and 120 with unknown origin.
Total article views: 1,800 (including HTML, PDF, and XML)
Thereof 1,710 with geography defined
and 90 with unknown origin.
Total article views: 487 (including HTML, PDF, and XML)
Thereof 457 with geography defined
and 30 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
25 citations as recorded by crossref.
- A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. 10.1016/j.rse.2021.112827
- The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China X. Zeng et al. 10.3390/toxics12010019
- A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
- Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP Y. Wang et al. 10.1016/j.isprsjprs.2021.03.018
- Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations J. Wei et al. 10.5194/acp-23-1511-2023
- 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
- 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
- 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
- Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China B. Chen et al. 10.1016/j.atmosenv.2023.120021
- Regional sources of NH3, SO2 and CO in the Third Pole B. Sharma et al. 10.1016/j.envres.2024.118317
- A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation Y. Wu et al. 10.1016/j.envint.2021.106576
- Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States M. El-Sayed et al. 10.1021/acs.est.3c01259
- 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
- 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
25 citations as recorded by crossref.
- A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. 10.1016/j.rse.2021.112827
- The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China X. Zeng et al. 10.3390/toxics12010019
- A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
- Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP Y. Wang et al. 10.1016/j.isprsjprs.2021.03.018
- Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations J. Wei et al. 10.5194/acp-23-1511-2023
- 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
- 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
- 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
- Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China B. Chen et al. 10.1016/j.atmosenv.2023.120021
- Regional sources of NH3, SO2 and CO in the Third Pole B. Sharma et al. 10.1016/j.envres.2024.118317
- A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation Y. Wu et al. 10.1016/j.envint.2021.106576
- Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States M. El-Sayed et al. 10.1021/acs.est.3c01259
- 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
- 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: 26 Jul 2024
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...
Altmetrics
Final-revised paper
Preprint