Pin-pointing and quantifying anthropogenic CH4 emissions from two landfill sites in Madrid, Spain, observed by a combination of passive, active, and in situ airborne measurements during the HALO CoMet 2.0 mission
Bibliographic record
Abstract
To reduce and mitigate anthropogenic greenhouse gas surface fluxes from industrial sites, their sources must be, firstly, identified or localized and, secondly, accurately quantified. For methane (CH4), the second most important anthropogenic greenhouse gas, the quantification of its diverse emitters is still a challenge. Due to their nature, these emitters can reach dimensions from point sources to hundreds of square kilometres for fossil fuel (gas, oil, coal) exploitation sites or up to several square kilometres in case of waste disposal sites. Although, CH4 emissions from, e.g., waste disposal sites can be computed from activity data combined with landfill models, a high potential for unintended and poorly quantified leakages remain due to, e.g., potential ruptures in the landfill cover. Consequently, the exact localization and quantification of those leakages is a necessary step towards reducing CH4 emissions from waste disposal sites.To have better knowledge and insights into anthropogenic and natural greenhouse gas emissions, a team of scientists has assembled a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research Aircraft) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. Although the campaign was primarily intended to observe and quantify CH4 and CO2 emissions and disentangle anthropogenic from natural sources at the high northern latitudes of Canada, a test flight over Spain revealed unexpectedly high and still persistent emissions from two landfills in Madrid - Valdemingomez and Pinto, previously also pointed out in an ESA story based on satellite observations. Both were investigated by means of passive and active remote sensing, as well as in situ airborne techniques.The measurements of the passive airborne remote sensing instrument MAMAP2D-Light, developed at the University of Bremen, delivers atmospheric concentration anomaly maps of CH4 and CO2. Here, its imaging capabilities are used to pin-point the origin of the CH4 emissions across the targeted landfills and to quantify their emissions. MAMAP2D-Light’s concentration maps are combined with highly accurate CH4 column concentration measurements from the integrated-path differential-absorption lidar CHARM-F (CO2 and CH4 Remote Monitoring-Flugzeug), developed by German Aerospace Center (DLR) in Oberpfaffenhofen. Additionally, airborne CH4 in situ mole fractions were measured by the Jena Instrument for Greenhouse Gases (JIG) and supplemented with wind data within the emission plume in order to complement the remote sensing observations.This contribution will present top-down emission estimates from measurements of all aforementioned instruments, operated quasi-simultaneously, i.e. within a time span of approximate 2 hours, over the targeted area in Madrid in August 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".