Articles | Volume 24, issue 5
https://doi.org/10.5194/acp-24-3009-2024
© Author(s) 2024. 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-24-3009-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Fan Lu
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Jason Blake Cohen
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
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We analyze global measurements of aerosol height from fires. A plume rise model reproduces measurements with a low bias in five regions, while a statistical model based on satellite measurements of trace gasses co-emitted from the fires reproduces measurements without bias in eight regions. We propose that the magnitude of the pollutants emitted may impact their height and subsequent downwind transport. Using satellite data allows better modeling of the global aerosol distribution.
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Short summary
We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground, overground, and abandoned mines. Mine-by-mine gas and flux data and 30 min observations from a flux tower located next to a mine shaft are integrated. The observed variability and bias correction are propagated over the emissions dataset, demonstrating that daily observations may not cover the range of variability. Comparisons show both an emissions magnitude and spatial mismatch with current inventories.
We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground,...
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