the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Identification and Quantification of CH4 Emissions from Madrid Landfills using Airborne Imaging Spectrometry and Greenhouse Gas Lidar
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 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 (MAMAP2DL) and active lidar (CHARM-F) remote sensing aboard the German research aircraft HALO, supported by in situ instruments, to quantify CH4 emissions. Using the CH4 column data and ECMWF-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 the 4th August 2022, with the highest CH4 column anomalies observed over active landfill areas in the vicinity of Madrid, Spain. Total emissions are estimated at ~13 th-1. Single co-located plume crossings from both instruments agree well within 1.2 th-1 (or 13 %). Flux errors range from ~25 to 40 %, mainly due to boundary layer and wind speed variability. This case study not only showcases the capabilities of applying a simple but fast cross-sectional mass balance approach, as well as its limitations due to challenging atmospheric boundary layer conditions, but also demonstrates the, to our knowledge, first successful use of both active and passive airborne remote sensing to quantify methane emissions from hot spots and independently verify their emissions.
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RC1: 'Comment on egusphere-2024-3182', Anonymous Referee #1, 10 Jan 2025
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The manuscript by Krautwurst et al. deals with an airborne campaign to characterise methane emissions from two landfills close to the Madrid city. The MAMAP2DL and CHARM-F instruments (pushbroom imaging spectrometer and active lidar, resp.) were flown over the landfills to derive maps of methane column concentrations, which were used to estimate emission rates.
The manuscript is very well written and presented, the methods are sound, and the results are very solid. On the downside, perhaps the level of novelty is not great, given that the high methane emissions from those landfills are well known, and the two instruments and the corresponding processing methods are already described in a number of peer-reviewed publications. For example, I miss some synergistic application of the two instruments.
Having said that, I recommend publication of this manuscript in ACP because of the growing interest in the development of methods for the monitoring of methane emissions and of the great technical quality of the study.
Below I am listing a number of minor points to be addressed at the authors’ discretion.
P1, L18: the definition of the ERF is probably not needed
P2, L28: “is produced”?
P3, L56-57: thermal imagers can also be “passive remote sensing imaging instruments”
P4, L115: “The non-operating …” verb missing?
P5, Fig 1 caption: “Spain” → “Iberian Peninsula”
P7, Fig 2 caption (and elsewhere): I had never heard the term “ground scene size”. I think is referring to “ground sampling distance” or “pixel size”?
P13, Eq. 4: please consider to use shorter variable names / subscripts for the emission rates F
P15, L374: the “different opening angles of the two instruments” are provided as the main reason for the difference in the flux estimates by the two instruments. Can we expect that factor to be more important than potential differences in the column concentration retrievals (there are some in Fig. 6), and the different atmospheric paths sampled by the two instruments?
P23, L533: I don’t think that is true. For example, Frankenberg et al. mapped methane missions with the AVIRIS-NG (optical) and HyTes (thermal) imaging spectromenters https://doi.org/10.1073/pnas.1605617113
P24, L562: “influence”
Citation: https://doi.org/10.5194/egusphere-2024-3182-RC1
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