Quantitative estimate of sources of uncertainty in drone-based methane emission measurements

Abstract. Site level measurements of methane emissions are used by operators for reconciliation with bottom-up emission inventories with the aim to improve accuracy, thoroughness and confidence in reported emissions. In that context it is of critical importance to avoid measurement errors, and to understand the measurement uncertainty. Remotely piloted aircraft systems (commonly referred to as ‘drones’) can play a pivotal role in the quantification of site-level methane emissions. Typical implementations use the ‘mass balance method’ to quantify emissions, with a high-precision methane sensor mounted on a quadcopter drone flying in a vertical curtain pattern; the total mass emission rate can then be computed post hoc from the measured methane concentration data and simultaneous wind data. Controlled release tests have shown that errors with the mass balance method can be considerable. For example, Liu et al. (2023) report absolute errors for more than 100 % for the two drone solutions tested; on the other hand, errors can be much smaller, of the order of 16 % root-mean-square errors in Corbett Smith (2022), if additional constraints are placed on the data, restricting the analysis to cases where the wind field was steady.In this paper we present a systematic error analysis of physical phenomena affecting the error in the mass balance method for parameters related to the acquisition of methane concentration data and to postprocessing. The sources of error are analysed individually, and it must be realised that individual errors can accumulate in practice, and they can also be augmented by other sources that are not included in the present work. Examples of these sources include the uncertainty in methane concentration measurements by a sensor with finite precision or the method used to measure the unperturbed wind velocity at the position of the drone. We find that the most important source of error considered is the horizontal and vertical spacings in the data acquisition as a coarse spacings can results in missing a methane plume. The potential error can be as high as 100 % in situations where the wind speed is steady and the methane plume has a coherent shape, contradicting the intuition of some operators in the industry. The likelihood of the extent of this error can be expressed in terms of a dimensionless number defined by the spatial resolution of the methane concentration measurements and the downwind distance from the main emission sources. The learnings from our theoretical error analysis are then applied to a number of historical measurements in a controlled release setting. We show how the learnings on the main sources of error can be used to eliminate potential errors during the postprocessing of flight data. Second, we evaluate an aggregated data set of 1,001 historical drone flights; our analysis shows that the potential errors in the mass balance method can be of the order of 100 % on occasions, even though the individual errors can be much smaller in the vast majority of the flights. The discussion section provides some guidelines to industry on how to avoid or minimize potential errors in drone measurements for methane emission quantification.