Articles | Volume 22, issue 20
https://doi.org/10.5194/acp-22-13881-2022
© 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-22-13881-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieving CH4-emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model
Tianqi Shi
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
Zeyu Han
School of Mathematics and Statistics, Wuhan University, Luoyu Road
No. 129, Wuhan 430079, China
Ge Han
School of Remote Sensing and Information Engineering, Wuhan
University, Luoyu Road No. 129, Wuhan 430079, China
Xin Ma
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, School of Atmospheric Sciences, Nanjing University,
Nanjing, China
Centre for Isotope Research, Energy and Sustainability Institute
Groningen (ESRIG), University of Groningen, Groningen, the Netherlands
Truls Andersen
Centre for Isotope Research, Energy and Sustainability Institute
Groningen (ESRIG), University of Groningen, Groningen, the Netherlands
Huiqin Mao
Ministry of Ecology and Environment Center for Satellite Application
on Ecology and Environment, Beijing, China
Cuihong Chen
Ministry of Ecology and Environment Center for Satellite Application
on Ecology and Environment, Beijing, China
Haowei Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
Wei Gong
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
Electronic Information School, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China
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10 citations as recorded by crossref.
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- Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements T. Andersen et al. 10.5194/acp-23-5191-2023
- High potential for CH4 emission mitigation from oil infrastructure in one of EU's major production regions F. Stavropoulou et al. 10.5194/acp-23-10399-2023
- Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years D. Fosco et al. 10.1016/j.scitotenv.2024.173981
- Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis T. Shi et al. 10.1016/j.enconman.2023.117346
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
- Quantifying factory-scale CO2/CH4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China T. Shi et al. 10.1088/1748-9326/acbce7
- Spectral Energy Model-Driven Inversion of XCO2 in IPDA Lidar Remote Sensing H. Zhang et al. 10.1109/TGRS.2023.3238117
- Efficacy of the CO Tracer Technique in Partitioning Biogenic and Anthropogenic Atmospheric CO2 Signals in the Humid Subtropical Eastern Highland Rim City of Cookeville, Tennessee W. Gichuhi & L. Gamage 10.3390/atmos14020208
- Mapping of Pollution Distribution for Electric Power System Based on Satellite Remote Sensing Y. Ma et al. 10.3389/fenvs.2022.938806
9 citations as recorded by crossref.
- A Method for Assessing Background Concentrations near Sources of Strong CO2 Emissions Q. Sun et al. 10.3390/atmos14020200
- Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements T. Andersen et al. 10.5194/acp-23-5191-2023
- High potential for CH4 emission mitigation from oil infrastructure in one of EU's major production regions F. Stavropoulou et al. 10.5194/acp-23-10399-2023
- Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years D. Fosco et al. 10.1016/j.scitotenv.2024.173981
- Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis T. Shi et al. 10.1016/j.enconman.2023.117346
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
- Quantifying factory-scale CO2/CH4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China T. Shi et al. 10.1088/1748-9326/acbce7
- Spectral Energy Model-Driven Inversion of XCO2 in IPDA Lidar Remote Sensing H. Zhang et al. 10.1109/TGRS.2023.3238117
- Efficacy of the CO Tracer Technique in Partitioning Biogenic and Anthropogenic Atmospheric CO2 Signals in the Humid Subtropical Eastern Highland Rim City of Cookeville, Tennessee W. Gichuhi & L. Gamage 10.3390/atmos14020208
1 citations as recorded by crossref.
Latest update: 04 Nov 2024
Short summary
CH4 works as the second-most important greenhouse gas, its reported emission inventories being far less than CO2. In this study, we developed a self-adjusted model to estimate the CH4 emission rate from strong point sources by the UAV-based AirCore system. This model would reduce the uncertainty in CH4 emission rate quantification accrued by errors in measurements of wind and concentration. Actual measurements on Pniówek coal demonstrate the high accuracy and stability of our developed model.
CH4 works as the second-most important greenhouse gas, its reported emission inventories being...
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