the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jul 2025
Quantifying agricultural N2O and CH4 emissions in the Netherlands using an airborne eddy covariance system
Abstract. This study reports on the first successful deployment of a new airborne eddy covariance (EC) setup to better characterize and quantify non-CO2 greenhouse gas emissions from agriculture. The system was deployed aboard the DLR research aircraft Cessna Caravan to quantify growing-season emissions of methane (CH4) and nitrous oxide (N2O) in Friesland, an agricultural region in the Netherlands, in early summer 2023. The EC system consists of a commercial quantum cascade laser spectrometer, specifically adapted for airborne observations and providing 10 Hz data of N2O and CH4, and the meteorological measurement suite METPOD, delivering data of the vertical wind, horizontal winds, water vapor and temperature. Our measurements are a novelty for N2O, since they are the first implementation of quantifying agricultural emissions with airborne EC, combining the advantages of regional-scale coverage, while maintaining high spatial resolution and hence are well suited to capture the spatial complexity of this dominant emission sector. The system provides fluxes with minimal low- and high-frequency distortions, low detection limits, and total uncertainties (30−100 %) comparable to other airborne methods, despite the complexity of agricultural emissions. During measurements in Friesland, we identified clear N2O emission hotspots and hot-moments, with peak fluxes of 0.34 µg m−2 s−1 on the regional-scale after intensive precipitation following a relatively dry period. Single small-scale hotspot emissions were as high as 1 µg m−2 s−1. In contrast, CH4 fluxes showed less temporal variations around a mean flux of 1.62 µg m−2 s−1 throughout the three-week campaign. N2O emissions were relatively high compared to other agricultural regions worldwide, and preliminary comparisons with EDGAR v8.0 and the Dutch emission inventory Emissieregistratie suggest substantial underestimation of growing-season N2O emissions in current inventories and the lack of an appropriate annual cycle. Our results further document the urgent need for independent verification of reported N2O and CH4 emissions from agriculture, which is the most dominant anthropogenic sector of non-CO2 greenhouse gas emissions and is expected to become even more dominant in the future, with an increasing world population and food demand.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3297', Anonymous Referee #1, 25 Aug 2025
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RC2: 'Comment on egusphere-2025-3297', Anonymous Referee #2, 03 Sep 2025
The authors describe high-speed airborne in situ measurements of N2O and CH4 using a new commercial spectrometer. These measurements are used to calculate vertical fluxes of N2O and CH4 via eddy covariance, and results are described for four flights during the GHGMon campaign in 2023. Results show a persistent level of CH4 flux consistent with ruminant emissions and increased N2O flux within a day of a significant rain event, with evidence that N2O flux increases is related to an increase but not amount of soil moisture. I find this work to be novel, well written, and worthy of publication in Atmospheric Chemistry and Physics. I recommend publication after the authors address a few minor comments:
Section 2: Placing the instrument and campaign description subsections before the details of the eddy covariance analysis may be a better way to organize the paper. I found myself jumping down to the instrument & campaign section and finally just read it totally before going back to the EC section.
Line 135: You denote stationarity, horizontal homogeneity, and well-developed turbulence as core EC assumptions, and I think you demonstrate each of these at different points, but the explicit terminology doesn’t come back. I’m not sure the exact place(s) where it would be appropriate, but it would be good somewhere in the manuscript to denote exactly where you are justifying these assumptions (e.g. sect 3.1 for stationarity, see comment below).
Line 160: Upcoming manuscript that by now may be citable? Or an upcoming experiment? It may suffice to simply state that you used Equation 4 and deeper footprint analysis is deemed outside the scope of this manuscript.
Line 167: This line about the timing is important to include, but was confusing placed here in the middle of a discussion of empty data treatment (made me ask if you were interpolating GPS time synchronization data).
Line 240: Should “no” be “minimal” or “no significant”? ogives give no further contributions only when they reach the highest/lowest frequency at 1 or 0…so maybe once contributions reach N%?
Line 301: What WMO scale is your dataset traceable to, presumably X2006A?
Line 310: Have you examined whether there is any artifact from aircraft motion in the measurements? If there is, then preferentially calibrating in turns could bias the dataset.
Line 311: Have you evaluated whether transferring standard gases affects the in-flight concentrations? Usually when standards are filled, there is some amount of settling time.
Line 315: Did you evaluate whether a calibration slope correction is needed, even if you could not perform this in the air (e.g. in the lab, on the ground with other calibration standards).
Line 322: This seems important to evaluate the measurements, there should at a minimum be a reference to this other publication, at least a conference proceeding/presentation.
Line 334: “measured with a time resolution” -> “reported at”, 100 Hz is not the sampling rate of any of the METPOD suite, just the sampling rate.
Line 407: If I understand correctly, this comparison is a pseudo-test of stationarity, but I don’t see you mention that directly.
Figure 4: It would be nicer to the eye for the x scales to be the same for each panel.
Line 421: 100 Hz is the data rate but not the time response of METPOD (see comment above). I couldn’t find in the reference the exact time response for the METPOD TAT measurement (I only saw a power spectrum for the wind components), but I’ve never seen a PT100 TAT sensor with a faster response than 7-8 Hz. Acknowledged that this does not affect the point you are making with the converging ogives by 0.3 Hz.
Line 446: Can you tell if the GHG sensor or wind sensor are the limiting noise source, or if they both contribute similarly?
Figure 7 caption: forenoon -> morning
Line 530: Figur -> Figure
Citation: https://doi.org/10.5194/egusphere-2025-3297-RC2
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The present study "Quantifying agricultural N2O and CH4 emissions in the Netherlands using an airborne eddy covariance system" describes in detail a measurement system and airborne eddy covariance method for quantifying CH4 and N2O fluxes over an agricultural region. The study is generally clearly presented and extremely detailed in methodology. Novel findings of N2O emissions from agriculture and comparison to inventories are also presented. The authors thoroughly consider uncertainties and potential biases in the measurements. I recommend publication following minor revisions.
Line 32: I believe the current IPCC recommendation for the GWP100 of biogenic CH4 is 27.
General comments/questions, largely for the purposes of improved clarity:
1: I assume that the choice of 90 s was at least partially made to capture all of the eddy scales based on ogives, analysis of the cospectral power, and/or integral timescale. I don't believe an explanation of this was explicitly given in the manuscript. It would be helpful to see in the text a description of what factors went into to choosing the 90 s windows.
2: I found it generally confusing what is meant by leg, vs Flight leg, vs flux segment. It would be useful if clear definitions were explicitly given and/or more consistency in the language were used. e.g. Do these terms always refer to the flight leg, or sometimes to the 90 s intervals as it seemed?
3. The authors mention that spatial homogeneity is required for eddy covariance, but the flux variations over a leg seem to indicate non-homogeneity. I would assume the condition of homogeneity is only necessary over the 90 s windows use for the flux calculations. Does the overlapping windows further loosen this condition? Some discussion of this in the text would be useful.
4. Are the LODs calculated for each 90 s segment (i.e. N represents the number of observations per 90 s flux interval)? If so, are these simply averaged over the flight? If, rather, N is the entire leg, wouldn't this calculation of LOD underestimate the ability of the instrumentation to distinguished spatially-resolved fluxes? In general I think more clarification is needed to contextualize the LODs reported.
5. It would be helpful to have a figure on the flux divergence calculation in the Appendix.
6. Authors mention recent studies utilizing the continuous wavelet transform method, which is often thought of as better for obtaining higher spatial resolution. Is there a reason that the authors used the moving window method instead?