Drone-based methane emission estimation at waste, livestock and energy facilities: Experimental tests in Italy and...

Methane (CH₄) is a potent greenhouse gas, and accurate facility-scale monitoring is essential for targeted mitigation. Current methods often rely on stationary sensors, which have limited sampling density and may fail to capture short-term or spatial variability in emission plumes. Drone-based monitoring has gained increasing attention, but most applications use point sensors. Open-path sensors mounted on drones offer higher data density and spatially integrated concentration measurements, yet their use for quantitative emission estimation remains poorly explored - particularly in associating integrated concentrations with wind data for mass-balance approaches. To address this gap, a combined methodology of numerical simulations and field campaigns was employed at three Italian sites - a landfill, a cattle farm, and a natural gas compression facility - to evaluate how different wind-averaging strategies influence emission estimates, providing novel insights into the reliability of drone-based open-path approaches. Simulations with WindTrax 2.0 showed that under stable conditions, emission estimates are more reproducible and easier to correct with models or empirical factors, though less precise in absolute terms. Comparing two wind averaging approaches - over the entire air column versus only the plume-intersected portion - demonstrated that averaging within the plume yields more stable results with narrower uncertainty intervals. Averaging over the entire column systematically overestimated fluxes, reaching 130 % at the compression facility, 116 % at the landfill, and 108 % at the farm. A third approach, averaging wind between successive transects, further improved stability, precision, and agreement with computational models. These findings highlight the potential of drone-based open-path systems for facility-level CH₄ monitoring, overcoming the sampling-density limitations of point sensors. Careful calibration and optimisation of wind models improve estimate reliability, with the best-performing approach in this study achieving an error of just 11 % relative to expected values. Overall, this demonstrates that appropriate treatment of wind data enables robust and repeatable CH₄ quantification across multiple types of facilities, providing a practical framework for accurate emission monitoring.