Methane (CH4) emissions from natural gas, waste, and industrial sources are routinely detected by satellite and aerial platforms; however, quantifying these plumes remains challenging due to their complex structure and rapidly changing fine-scale atmospheric dynamics. This study directly addresses the resulting uncertainties in UAV flight measurements by employing the fire dynamics simulation (FDS) in large eddy simulation (LES) mode, leveraging mean wind data derived from LiDAR data sets. The FDS model was validated with in situ CH4 concentration data collected by uncrewed aerial vehicles (UAVs) during controlled release experiments. Our analysis of 10 extensively sampled plumes shows that FDS accurately reproduces the magnitude and spatiotemporal variations of the observed plumes for sufficiently large pipes (diameter >0.6 cm). We found that factors such as gas exit velocity, obstacles, and terrain topography significantly affect the near-field dynamics of the plumes. To a lesser extent, the temperature of the gas influences plume behavior at higher mass flow rates. We highlight the added value of high-resolution LES modeling to understand CH4 plume dynamics captured by various sensors, aiming to improve current emission quantification methods.
Yuvaraj et al. (2026) studied this question.