Articles | Volume 15, issue 3
https://doi.org/10.5194/amt-15-721-2022
© 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/amt-15-721-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2022
Research article |
| 09 Feb 2022
| 09 Feb 2022
Automated detection of atmospheric NO2 plumes from satellite data: a tool to help infer anthropogenic combustion emissions
National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Paul I. Palmer
National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
School of GeoSciences, University of Edinburgh, Edinburgh, UK
Tianran Zhang
National Centre for Earth Observation, Kings College London, London, UK
now at: Satellite Vu, London, UK
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Cited
21 citations as recorded by crossref.
- Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1 M. Goudar et al. 10.5194/gmd-16-4835-2023
- CLASP: CLustering of Atmospheric Satellite Products and Its Applications in Feature Detection of Atmospheric Trace Gases T. Lee & Y. Wang 10.1029/2023JD038887
- Sensitivity analysis for the detection of NO2 plumes from seagoing ships using TROPOMI data S. Kurchaba et al. 10.1016/j.rse.2024.114041
- Analyzing Local Carbon Dioxide and Nitrogen Oxide Emissions From Space Using the Divergence Method: An Application to the Synthetic SMARTCARB Dataset J. Hakkarainen et al. 10.3389/frsen.2022.878731
- Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2 D. Goldberg et al. 10.1016/j.rse.2023.113917
- Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA’s Black Marble product suite S. Chakraborty et al. 10.1088/1748-9326/acb6a7
- Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants J. Dumont Le Brazidec et al. 10.5194/gmd-16-3997-2023
- Exploring the potential of machine learning for leaf angle distribution type identification from leveled digital photography: A case study for broadleaf tree and shrub species M. Aun & J. Pisek 10.1016/j.agrformet.2023.109570
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. 10.3390/rs15204904
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- Role of space station instruments for improving tropical carbon flux estimates using atmospheric data P. Palmer et al. 10.1038/s41526-022-00231-6
- To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals T. Riess et al. 10.5194/amt-16-5287-2023
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm J. Tavora et al. 10.3389/fmars.2023.1215327
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data S. Kurchaba et al. 10.3390/rs14225809
- 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
- Building a bridge: characterizing major anthropogenic point sources in the South African Highveld region using OCO-3 carbon dioxide snapshot area maps and Sentinel-5P/TROPOMI nitrogen dioxide columns J. Hakkarainen et al. 10.1088/1748-9326/acb837
- Automatic retrieval of volcanic SO2 emission source from TROPOMI products B. Markus et al. 10.3389/feart.2022.1064171
- Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions K. Wu et al. 10.5194/amt-16-581-2023
21 citations as recorded by crossref.
- Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1 M. Goudar et al. 10.5194/gmd-16-4835-2023
- CLASP: CLustering of Atmospheric Satellite Products and Its Applications in Feature Detection of Atmospheric Trace Gases T. Lee & Y. Wang 10.1029/2023JD038887
- Sensitivity analysis for the detection of NO2 plumes from seagoing ships using TROPOMI data S. Kurchaba et al. 10.1016/j.rse.2024.114041
- Analyzing Local Carbon Dioxide and Nitrogen Oxide Emissions From Space Using the Divergence Method: An Application to the Synthetic SMARTCARB Dataset J. Hakkarainen et al. 10.3389/frsen.2022.878731
- Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2 D. Goldberg et al. 10.1016/j.rse.2023.113917
- Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA’s Black Marble product suite S. Chakraborty et al. 10.1088/1748-9326/acb6a7
- Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants J. Dumont Le Brazidec et al. 10.5194/gmd-16-3997-2023
- Exploring the potential of machine learning for leaf angle distribution type identification from leveled digital photography: A case study for broadleaf tree and shrub species M. Aun & J. Pisek 10.1016/j.agrformet.2023.109570
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions K. Wu et al. 10.3390/rs15204904
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. 10.5194/gmd-17-4773-2024
- Role of space station instruments for improving tropical carbon flux estimates using atmospheric data P. Palmer et al. 10.1038/s41526-022-00231-6
- To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals T. Riess et al. 10.5194/amt-16-5287-2023
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm J. Tavora et al. 10.3389/fmars.2023.1215327
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Supervised Segmentation of NO2 Plumes from Individual Ships Using TROPOMI Satellite Data S. Kurchaba et al. 10.3390/rs14225809
- 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
- Building a bridge: characterizing major anthropogenic point sources in the South African Highveld region using OCO-3 carbon dioxide snapshot area maps and Sentinel-5P/TROPOMI nitrogen dioxide columns J. Hakkarainen et al. 10.1088/1748-9326/acb837
- Automatic retrieval of volcanic SO2 emission source from TROPOMI products B. Markus et al. 10.3389/feart.2022.1064171
- Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions K. Wu et al. 10.5194/amt-16-581-2023
Latest update: 29 Jun 2024
Short summary
We developed a machine learning model to detect plumes of nitrogen dioxide satellite observations over 2 years. We find over 310 000 plumes, mainly over cities, industrial regions, and areas of oil and gas production. Our model performs well in comparison to other datasets and in some cases finds emissions that are not included in other datasets. This method could be used to help locate and measure emission hotspots across the globe and help inform climate policies.
We developed a machine learning model to detect plumes of nitrogen dioxide satellite...
