Articles | Volume 22, issue 17
https://doi.org/10.5194/acp-22-11505-2022
© Author(s) 2022. 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-22-11505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Sep 2022
Research article |
| 07 Sep 2022
| 07 Sep 2022
Quantifying NOx emissions in Egypt using TROPOMI observations
Anthony Rey-Pommier
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Grégoire Broquet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Theodoros Christoudias
The Cyprus Institute, Climate and Atmosphere Research Center, 2121 Nicosia, Cyprus
Jonilda Kushta
The Cyprus Institute, Climate and Atmosphere Research Center, 2121 Nicosia, Cyprus
Didier Hauglustaine
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Jean Sciare
The Cyprus Institute, Climate and Atmosphere Research Center, 2121 Nicosia, Cyprus
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Cited
11 citations as recorded by crossref.
- Artificial intelligence‐driven insights: Precision tracking of power plant carbon emissions using satellite data Z. Zhang et al. 10.1049/enc2.12129
- Identification of NO emissions and source characteristics by TROPOMI observations – A case study in north-central Henan, China H. Sheng et al. 10.1016/j.scitotenv.2024.172779
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Quantification of carbon monoxide emissions from African cities using TROPOMI G. Leguijt et al. 10.5194/acp-23-8899-2023
- Ozone production over arid regions: insights into meteorological and chemical drivers M. Mirrezaei et al. 10.1088/2515-7620/ad484c
- Detecting nitrogen oxide emissions in Qatar and quantifying emission factors of gas-fired power plants – a 4-year study A. Rey-Pommier et al. 10.5194/acp-23-13565-2023
- Analyzing nitrogen dioxide to nitrogen oxide scaling factors for data-driven satellite-based emission estimation methods: A case study of Matimba/Medupi power stations in South Africa J. Hakkarainen et al. 10.1016/j.apr.2024.102171
- Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. 10.1016/j.ese.2024.100425
- Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique Q. Tu et al. 10.5194/amt-16-2237-2023
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. 10.5194/acp-24-7667-2024
- Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic C. Lonsdale & K. Sun 10.5194/acp-23-8727-2023
11 citations as recorded by crossref.
- Artificial intelligence‐driven insights: Precision tracking of power plant carbon emissions using satellite data Z. Zhang et al. 10.1049/enc2.12129
- Identification of NO emissions and source characteristics by TROPOMI observations – A case study in north-central Henan, China H. Sheng et al. 10.1016/j.scitotenv.2024.172779
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Quantification of carbon monoxide emissions from African cities using TROPOMI G. Leguijt et al. 10.5194/acp-23-8899-2023
- Ozone production over arid regions: insights into meteorological and chemical drivers M. Mirrezaei et al. 10.1088/2515-7620/ad484c
- Detecting nitrogen oxide emissions in Qatar and quantifying emission factors of gas-fired power plants – a 4-year study A. Rey-Pommier et al. 10.5194/acp-23-13565-2023
- Analyzing nitrogen dioxide to nitrogen oxide scaling factors for data-driven satellite-based emission estimation methods: A case study of Matimba/Medupi power stations in South Africa J. Hakkarainen et al. 10.1016/j.apr.2024.102171
- Trends and drivers of anthropogenic NO emissions in China since 2020 H. Li et al. 10.1016/j.ese.2024.100425
- Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique Q. Tu et al. 10.5194/amt-16-2237-2023
- A lightweight NO2-to-NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations S. Meier et al. 10.5194/acp-24-7667-2024
- Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic C. Lonsdale & K. Sun 10.5194/acp-23-8727-2023
Latest update: 02 Nov 2024
Short summary
Emission inventories for air pollutants can be uncertain in developing countries. In order to overcome these uncertainties, we model nitrogen oxide emissions in Egypt using satellite retrievals. We detect a weekly cycle reflecting Egyptian social norms, an annual cycle consistent with electricity consumption and an activity drop due to the COVID-19 pandemic. However, discrepancies with inventories remain high, illustrating the needs for additional data to improve the potential of our method.
Emission inventories for air pollutants can be uncertain in developing countries. In order to...
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