Identification and quantification of CH <sub>4</sub> emissions from Madrid landfills using airborne imaging spectrometry and greenhouse gas lidar
Sven Krautwurst, C. Fruck, Sebastian Wolff, Jakob Borchardt, Oke Huhs, Konstantin Gerilowski, Michał Gałkowski, Christoph Kiemle, Mathieu Quatrevalet, Martin Wirth, Christian Mallaun, John P. Burrows, Christoph Gerbig, Andreas Fix, Hartmut Boesch, H. Bovensmann
Abstract
Abstract. Methane (CH4), alongside carbon dioxide (CO2), is a key driver of anthropogenic climate change. Reducing CH4 is crucial for short-term climate mitigation. Waste-related activities, such as landfills, are a major CH4 source, even in developed countries. Atmospheric concentration measurements using remote sensing (RS) offer a powerful way to quantify these emissions. We study waste facilities near Madrid, Spain, where satellite data indicated high CH4 emissions. For the first time, we combine passive imaging (Methane Airborne Mapper 2D – Light, MAMAP2DL) and active lidar (CO2 and CH4 Atmospheric Remote Monitoring – Flugzeug, CHARM-F) remote sensing aboard the German High Altitude and Long Range Research Aircraft (HALO), supported by in situ instruments, to quantify CH4 emissions. Using the CH4 column data and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5) model wind information validated by airborne measurements, we estimate landfill emissions through a cross-sectional mass balance approach. Strong emission plumes are traced up to 20 km downwind on 4 August 2022, with the highest CH4 column anomalies observed over active landfill areas in the vicinity of Madrid, Spain. Total emissions are estimated to be up to ∼ 13 t h−1. Single co-located plume crossings from both instruments agree well within 1.2 t h−1 (or 13 %). Flux errors range from ∼ 25 % to 40 %, mainly due to boundary layer (BL) and wind speed variability. This case study not only showcases the capabilities of applying a simple but fast cross-sectional mass balance approach, along with its limitations due to challenging atmospheric boundary layer conditions, but also demonstrates, to our knowledge, the first successful use of both active and passive airborne remote sensing to quantify methane emissions from hotspots and independently verify their emissions.