Articles | Volume 22, issue 15
https://doi.org/10.5194/acp-22-9747-2022
© 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-22-9747-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying CH4 emissions in hard coal mines from TROPOMI and IASI observations using the wind-assigned anomaly method
Qiansi Tu
CORRESPONDING AUTHOR
School of Mechanical Engineering, Tongji University, Shanghai, China
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Matthias Schneider
CORRESPONDING AUTHOR
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Frank Hase
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Farahnaz Khosrawi
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Benjamin Ertl
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Karlsruhe Institute of Technology, Steinbuch Centre for Computing
(SCC), Karlsruhe, Germany
Jaroslaw Necki
AGH – University of Science and Technology, Krakow, Poland
Darko Dubravica
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Christopher J. Diekmann
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
now at: Software Solutions Department, Telespazio Germany GmbH, Darmstadt, Germany
Thomas Blumenstock
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
Dianjun Fang
School of Mechanical Engineering, Tongji University, Shanghai, China
Qingdao Sino-German Institute of Intelligent Technologies, Qingdao,
China
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Cited
9 citations as recorded by crossref.
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- 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
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. 10.1016/j.scitotenv.2023.164701
- 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
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method Q. Tu et al. 10.5194/acp-24-4875-2024
- Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. 10.5194/acp-24-10441-2024
- Unveiling nitrogen oxide emissions from open-pit copper mines through satellite observations I. Ialongo et al. 10.1088/1748-9326/adb767
9 citations as recorded by crossref.
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- 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
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. 10.1016/j.scitotenv.2023.164701
- 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
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method Q. Tu et al. 10.5194/acp-24-4875-2024
- Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. 10.5194/acp-24-10441-2024
- Unveiling nitrogen oxide emissions from open-pit copper mines through satellite observations I. Ialongo et al. 10.1088/1748-9326/adb767
Latest update: 09 Mar 2025
Short summary
Three-year satellite observations and high-resolution model forecast of XCH4 are used to derive CH4 emissions in the USCB region, Poland – a region of intense coal mining activities. The wind-assigned anomalies for two opposite wind directions are calculated and the estimated emission rates are very close to the inventories and in reasonable agreement with the previous studies. Our method is quite robust and can serve as a simple method to estimate CH4 or CO2 emissions for other regions.
Three-year satellite observations and high-resolution model forecast of XCH4 are used to derive...
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