Articles | Volume 19, issue 20
https://doi.org/10.5194/acp-19-12835-2019
© 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-19-12835-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Oct 2019
Research article |
| 15 Oct 2019
| 15 Oct 2019
High-resolution (0.05° × 0.05°) NOx emissions in the Yangtze River Delta inferred from OMI
Hao Kong
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Ruixiong Zhang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
now at: School of Earth and Atmospheric Sciences, Georgia
Institute of Technology, Atlanta, GA, USA
Mengyao Liu
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Hongjian Weng
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Ruijing Ni
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Lulu Chen
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Jingxu Wang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, China
Yingying Yan
Department of Atmospheric Science, School of Environmental Sciences,
China University of Geosciences, Wuhan, China
Qiang Zhang
Ministry of Education Key Laboratory for Earth System Modeling,
Department of Earth System Science, Tsinghua University, Beijing,
China
Viewed
Total article views: 3,007 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jan 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,091 | 867 | 49 | 3,007 | 38 | 62 |
- HTML: 2,091
- PDF: 867
- XML: 49
- Total: 3,007
- BibTeX: 38
- EndNote: 62
Total article views: 2,332 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Oct 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,665 | 627 | 40 | 2,332 | 31 | 50 |
- HTML: 1,665
- PDF: 627
- XML: 40
- Total: 2,332
- BibTeX: 31
- EndNote: 50
Total article views: 675 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Jan 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 426 | 240 | 9 | 675 | 7 | 12 |
- HTML: 426
- PDF: 240
- XML: 9
- Total: 675
- BibTeX: 7
- EndNote: 12
Viewed (geographical distribution)
Total article views: 3,007 (including HTML, PDF, and XML)
Thereof 2,877 with geography defined
and 130 with unknown origin.
Total article views: 2,332 (including HTML, PDF, and XML)
Thereof 2,401 with geography defined
and -69 with unknown origin.
Total article views: 675 (including HTML, PDF, and XML)
Thereof 476 with geography defined
and 199 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.
- Machine learning-based estimation of ground-level NO2 concentrations over China Y. Chi et al. 10.1016/j.scitotenv.2021.150721
- Using a New Top‐Down Constrained Emissions Inventory to Attribute the Previously Unknown Source of Extreme Aerosol Loadings Observed Annually in the Monsoon Asia Free Troposphere S. Wang et al. 10.1029/2021EF002167
- Considerable Unaccounted Local Sources of NOx Emissions in China Revealed from Satellite H. Kong et al. 10.1021/acs.est.1c07723
- Characterization and Source Apportionment of Fine Particles during a Heavy Pollution Episode over the Yangtze River Delta, China L. Xia et al. 10.3390/atmos11070720
- Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis Y. Wang et al. 10.5194/acp-20-6631-2020
- Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China X. Hu et al. 10.1029/2020JD033538
- Development of an integrated machine-learning and data assimilation framework for NOx emission inversion Y. Chen et al. 10.1016/j.scitotenv.2023.161951
- Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. 10.3390/rs13091798
- Extending Ozone‐Precursor Relationships in China From Peak Concentration to Peak Time H. Qu et al. 10.1029/2020JD033670
- Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements Q. He et al. 10.1088/1748-9326/abc7df
- Evaluating the methods and influencing factors of satellite-derived estimates of NOX emissions at regional scale: A case study for Yangtze River Delta, China Y. Yang et al. 10.1016/j.atmosenv.2019.117051
- Improvement of the satellite-derived NO<sub><i>x</i></sub> emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in the Yangtze River Delta, China Y. Yang et al. 10.5194/acp-21-1191-2021
- Robust geographical detector Z. Zhang et al. 10.1016/j.jag.2022.102782
- Impacts of reduced deposition of atmospheric nitrogen on coastal marine eco-system during substantial shift in human activities in the twenty-first century F. Mumtaz et al. 10.1080/19475705.2021.1949396
- Pinpointing nitrogen oxide emissions from space S. Beirle et al. 10.1126/sciadv.aax9800
- Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling B. Zheng et al. 10.1016/j.scib.2020.12.008
- Estimating city NOX emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China R. Xue et al. 10.1016/j.uclim.2022.101150
- Synoptic condition-driven summertime ozone formation regime in Shanghai and the implication for dynamic ozone control strategies H. Luo et al. 10.1016/j.scitotenv.2020.141130
- Formation pathways and sources of size-segregated nitrate aerosols in a megacity identified by dual isotopes Y. Zhu et al. 10.1016/j.atmosenv.2021.118708
- Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations Y. Pan et al. 10.1016/j.scitotenv.2022.161157
- Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China X. Li et al. 10.5194/acp-23-8001-2023
- Assessment of surface ozone production in Qinghai, China with satellite-constrained VOCs and NOx emissions W. Li et al. 10.1016/j.scitotenv.2023.166602
- NOx Emission Reduction and Recovery during COVID-19 in East China R. Zhang et al. 10.3390/atmos11040433
- Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts Y. Wang et al. 10.5194/acp-20-6651-2020
- Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations R. Li et al. 10.3390/rs12061041
25 citations as recorded by crossref.
- Machine learning-based estimation of ground-level NO2 concentrations over China Y. Chi et al. 10.1016/j.scitotenv.2021.150721
- Using a New Top‐Down Constrained Emissions Inventory to Attribute the Previously Unknown Source of Extreme Aerosol Loadings Observed Annually in the Monsoon Asia Free Troposphere S. Wang et al. 10.1029/2021EF002167
- Considerable Unaccounted Local Sources of NOx Emissions in China Revealed from Satellite H. Kong et al. 10.1021/acs.est.1c07723
- Characterization and Source Apportionment of Fine Particles during a Heavy Pollution Episode over the Yangtze River Delta, China L. Xia et al. 10.3390/atmos11070720
- Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis Y. Wang et al. 10.5194/acp-20-6631-2020
- Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China X. Hu et al. 10.1029/2020JD033538
- Development of an integrated machine-learning and data assimilation framework for NOx emission inversion Y. Chen et al. 10.1016/j.scitotenv.2023.161951
- Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. 10.3390/rs13091798
- Extending Ozone‐Precursor Relationships in China From Peak Concentration to Peak Time H. Qu et al. 10.1029/2020JD033670
- Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements Q. He et al. 10.1088/1748-9326/abc7df
- Evaluating the methods and influencing factors of satellite-derived estimates of NOX emissions at regional scale: A case study for Yangtze River Delta, China Y. Yang et al. 10.1016/j.atmosenv.2019.117051
- Improvement of the satellite-derived NO<sub><i>x</i></sub> emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in the Yangtze River Delta, China Y. Yang et al. 10.5194/acp-21-1191-2021
- Robust geographical detector Z. Zhang et al. 10.1016/j.jag.2022.102782
- Impacts of reduced deposition of atmospheric nitrogen on coastal marine eco-system during substantial shift in human activities in the twenty-first century F. Mumtaz et al. 10.1080/19475705.2021.1949396
- Pinpointing nitrogen oxide emissions from space S. Beirle et al. 10.1126/sciadv.aax9800
- Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling B. Zheng et al. 10.1016/j.scib.2020.12.008
- Estimating city NOX emissions from TROPOMI high spatial resolution observations – A case study on Yangtze River Delta, China R. Xue et al. 10.1016/j.uclim.2022.101150
- Synoptic condition-driven summertime ozone formation regime in Shanghai and the implication for dynamic ozone control strategies H. Luo et al. 10.1016/j.scitotenv.2020.141130
- Formation pathways and sources of size-segregated nitrate aerosols in a megacity identified by dual isotopes Y. Zhu et al. 10.1016/j.atmosenv.2021.118708
- Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations Y. Pan et al. 10.1016/j.scitotenv.2022.161157
- Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China X. Li et al. 10.5194/acp-23-8001-2023
- Assessment of surface ozone production in Qinghai, China with satellite-constrained VOCs and NOx emissions W. Li et al. 10.1016/j.scitotenv.2023.166602
- NOx Emission Reduction and Recovery during COVID-19 in East China R. Zhang et al. 10.3390/atmos11040433
- Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts Y. Wang et al. 10.5194/acp-20-6651-2020
- Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations R. Li et al. 10.3390/rs12061041
Latest update: 24 Apr 2024
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(8475 KB) - Full-text XML
Short summary
We develop a computationally efficient space-based top-down method to inverting NOx emissions in major urban areas at high resolution. The inversion method uses long-term OMI NO2 data to enhance the horizontal resolution, and it accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition on NOx. The inversion results reveal fine-scale spatial information of emissions which is hardly captured by bottom-up inventories.
We develop a computationally efficient space-based top-down method to inverting NOx emissions in...
Altmetrics
Final-revised paper
Preprint