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The CoMet 2.0 Arctic airborne measurement campaign of 2022 targeted a variety of natural as well as anthropogenic sources of CH4, mostly in Canada, such as landfills, coal mines, power plants or fossil fuel exploitation sites. Many anthropogenic emission targets consist of a few strong emitters with small or negligible spatial extension. In these cases, emission plumes can readily be observed by passive imaging spectrometers, through the observed enhancement in column averaged CH4. However, over oil and gas fields such as the Lloydminster area at the Alberta/Saskatchewan border, with numerous individual wells extending over large areas, this is much more difficult since individual plumes are lower in magnitude and may even overlap. In such cases it may not be possible to resolve plumes from individual sources, but the total flux can still be estimated using a budget approach. Nevertheless, limitations arise from spatial and temporal variations in the wind field, regarding proper quantification of the source strengths. In this contribution we present our strategy for source attribution, combining measurements by the airborne CHARM-F greenhouse-gas lidar and the MAMAP2DL imaging spectrometer with emission inventories and inverse modeling. A similar approach has already been successfully applied to CHARM-F data recorded over the Upper Silesian Coal Basin during the CoMet 1.0 campaign. CHARM-F is an Integrated-Path Differential-Absorption (IPDA) lidar that provides vertical column concentrations of CO2 and CH4 up to the flight altitude along the flight track. The advantages of lidar are the insensitivity to illumination conditions and a low intrinsic bias. MAMAP2DL is a passive airborne push broom imaging spectrometer that measures spatially resolved changes in relative column concentrations of CH4 and CO2. During the CoMet 2.0 Arctic campaign in August and September 2022, CHARM-F and MAMAP2DL have been deployed onboard the German research aircraft HALO, alongside a suite of complementary instruments for in-situ measurements of CH4, CO2 and other trace gases. We introduce our methods for data treatment and inverse modelling and show first results from this approach.
Fruck et al. (Fri,) studied this question.