the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2024
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.
Competing interests: Christoph Gerbig has a competing interest as he is ACP editor (and CoMet special issue editor). Christoph Kiemle has a competing interest as he is an associate editor at AMT. The remaining authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
(14375 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2024-3182', Anonymous Referee #1, 10 Jan 2025
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 -
RC2: 'Comment on egusphere-2024-3182', Anonymous Referee #2, 03 Feb 2025
This manuscript by Krautwurst et al. (2024) shows the first use of both active lidar and passive remote sensing on aircraft to estimate landfill methane in Madrid with verification using in situ instruments. The manuscript is generally well written and thoroughly explains the instrumentation and methodologies used. The described research reflects important work to compare instrument capabilities, especially for complex emission sources like landfills. There is growing interest in tracking emissions reductions, and the work by Kraustwurst et al. will be valuable for establishing the methodologies available for accurate quantification of methane emissions.
The manuscript currently provides thorough description of instruments, retrievals, and flux estimates, and I recommend publication. The main recommended revision is to add some discussion of how sources on the landfill were identified. Currently, there is little discussion of how sources at the facility were geolocated and attributed and the uncertainties in these methodologies. As the identification of the sources at the landfill is a major conclusion, there needs to be some additional text added to the body of the manuscript explaining how this was done. Detailed comments:
- Line 34: Is this EU number for landfills? Solid waste? Or total waste sector? And can the actual reported number be added in addition to the relative 24% (as this would allow comparison to other regions of the world)?
- Line 43: If not specified later in the manuscript, it would be helpful to know if the Madrid facilities, specifically, use IPCC-type methods.
- Line 63: Some discussion of the value of active remote sensing in comparison to passive remote sensing would be valuable.
- Section 2.1.1: This description of CoMet 2.0 Arctic seems unrelated to the manuscript focus.
- Figure 1: A color key in the figure would be helpful.
- Line 121: Adding waste-in-place metrics for the 2 landfills and adding activity per year rates for both landfills (in the same units) would contextualize the landfills better.
- Line 125: Clarify if, for readers unfamiliar with EPRTR, there is potential for the facility reported numbers to be missing methane sources. For example, are some sources not reported due to regulatory limitations, or should we expect the reported number to be comparable to measurements?
- Line 132: Further information on winds would be valuable in this section. For example, visuals showing the wind speeds and directions over time and space for the study area (can be added to Appendix rather than body of text). Some discussion of topography may also be valuable. For example, is it possible that the second landfill location is a topographic low and the methane from upwind is just pooling there?
- Line 297: It’s unclear what the authors are defining as a “plume” versus “enhancement” and how this relates to the differentiation between “flux” and “emission rate”.
- Figure 5: It would be helpful to separate 5a-b from 5c-d and provide more explanation of what 5c-d is meant to show. Keys for the color outlines are needed. Were the 5c-d enhancements the highest enhancements of all observed methane, or just those above the landfills? Why do the shapes of enhancement regions vary and how were they delineated? This comment relates to the above comment about providing more details on how source locations were identified on the landfills.
- Line 354-357: These two sentences are confusing and contradictory. The authors point out a source but then say it may not be that source and could be any part of the landfill. Is this due some aspect of method uncertainty (e.g., geolocation of source, wind direction, identification of enhanced pixels)?
- Figure 7: The flux estimates above the Pinto landfill seem to show that the passive remote sensing is seeing a combination of background and enhancement from landfill whereas active lidar is only seeing the enhancement. Is this the influence of the narrower instrument view? Some discussion on the potential interpretation difficulties if the active lidar "misses" the enhancement when there are steep gradients (and/or need for tight flight patterns to avoid this) would be valuable.
- Section 3.4.2: Would it not be possible for the other methane sources in the region to be significantly contributing but not be individually detectable? For example, the enhancement from a single smaller source may not be detectable over that source but as multiple of these sources emit in your domain, could their combined methane lead to detectable enhancements downwind if the winds are stable? It would be valuable to provide some metrics (e.g., emission rate) of the expected potential contribution of the other methane sources in the domain.
- Line 448: This seems like a very important potential limitation of these methodologies, so it would be valuable to move the discussion of the wind changes into the body of the paper rather than the Appendix. A visual showing the potential location of these "puffs" in comparison to the mapped "plumes" would be helpful for understanding the impact this wind direction change could have on the calculated fluxes (this visual does not necessarily need to go in the body of the text though).
- Line 499: Why does the unpredictability of locations impact process-based modeling? In some cases, landfill operators have well documented information on which cells are being filled at different points in time. For the Madrid landfills, is it that this information is not being collected or just not publicly available to researchers?
- Line 525: It seems inaccurate to say the two landfill sites were "well separated" when the downwind site flux could not be isolated.
- Line 546-548: This implies confidence in the ability of these instruments to identify sub-facility locations of sources. If this is a major conclusion of this manuscript, it does not seem like there was enough explanation of the methodology for identifying source locations and the associated uncertainty in those locations in the body of the paper.
- Section 5: Given the mention of the coming satellite in the introduction, it may be valuable to have more discussion on how the information gained from this study can inform future methods for the satellite.
Citation: https://doi.org/10.5194/egusphere-2024-3182-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 351 | 114 | 51 | 516 | 17 | 15 |
- HTML: 351
- PDF: 114
- XML: 51
- Total: 516
- BibTeX: 17
- EndNote: 15
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 172 | 33 |
| Germany | 2 | 96 | 18 |
| Netherlands | 3 | 28 | 5 |
| China | 4 | 25 | 4 |
| Japan | 5 | 19 | 3 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 172