High-fidelity methane plume modelling over coal mines: Integrating Large Eddy Simulations (LES) with remote sensing imagery
Year: 2026
Type: preprint
Abstract: Methane (CH 4 ) emissions from coal mine shafts represent 33% of the global CH 4 fossil fuel emissions into the atmosphere, and are subject to stricter regulations drafted in recent years (e.g. EU, China). Different measurement strategies have been deployed to quantify the amounts of CH 4 released, primarily onboard aircraft and satellites. For the first time, we simulate the CH 4 plume dynamics at high resolution using Large Eddy Simulations (LES) forced by meteorological data, obstacles (such as trees and buildings), 1-m terrain topologies, and exact source characterisation. We validated the plume characteristics against path-integrated measurements from a ground-based spectrometer horizontally scanning the coal mine plume from Pniówek V (Poland). We demonstrate that the exit velocity of the mine shaft’s gas, its concentration and the turbulence conditions are the key drivers to reproduce the CH 4 plume behaviour and magnitude. We estimated the uncertainties to be 9% (±2.5 m) for the altitude of maximum values and 17% for the peak CH 4 enhancement, resulting mainly from wind speed fluctuations. For coal mine shaft plumes, precise source characterisation is critical to simulation accuracy, whereas high-resolution terrain and building data are less essential. This is a direct result of the high-momentum, CH 4 -rich discharge from the diffusion tower, which dictates the plume trajectory more than local surface geometry.
Source: ChemRxiv
Institutions: Centre National de la Recherche Scientifique, Environmental Defense Fund, Harvard University, Heidelberg University, AGH University of Krakow, Université de Versailles Saint-Quentin-en-Yvelines, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Laboratoire des Sciences du Climat et de l'Environnement
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7155105272
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