the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 May 2024
Using Open-Path Dual-Comb Spectroscopy to Monitor Methane Emissions from Simulated Grazing Cattle
Abstract. Accurate whole-farm or herd-level measurements of livestock methane emissions are necessary for anthropogenic greenhouse gas inventories and to evaluate mitigation strategies. A controlled methane (CH4) release experiment was performed to determine if dual comb spectroscopy (DCS) can detect CH4 concentration enhancements produced by a typical herd of beef cattle in an extensive grazing system. Open-path DCS was used to measure downwind and upwind CH4 concentrations from ten point-sources of methane simulating cattle emissions. The CH4 mixing ratio along with wind velocity data were used to calculate CH4 flux using an inverse dispersion model, and the simulated fluxes were then compared to the actual CH4 release rate. For a source located 60 m from the downwind path, the DCS system detected 10 nmol mol-1 CH4 horizontal concentration gradient above the atmospheric background concentration with a precision of 6 nmol mol-1 in 15-min interval. A CH4 release of 3970 g day-1 was performed resulting in an average concentration enhancement of 24 nmol mol-1 of CH4. The calculated CH4 flux was (4002±1498) g day‑1 in agreement with the actual release rate. Periodically altering the downwind path, which may be needed to track moving cattle, did not adversely affect the ability to determine the CH4 flux. The measurement was only limited by maintaining sufficient reflected power from the remote retroreflectors over the open path to achieve a sufficient signal-to-noise ratio. These results give us confidence that CH4 flux can be determined by grazing cattle with low disturbance and direct field-scale measurements.
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RC1: 'Comment on egusphere-2024-1181', Anonymous Referee #1, 03 Jun 2024
Review of “Using Open-Path Dual-Comb Spectroscopy to Monitor Methane Emissions from Simulated Grazing Cattle” by Weerasekara et al., submitted to Atmospheric Measurement Techniques.
General Comments
This manuscript examines the potential of a line-averaging gas sensor (based on dual comb spectroscopy, DCS) for use in calculating methane (CH4) emissions from grazing cattle. This is an important topic as grazing cattle are significant CH4 emitters, and a sensitive and robust CH4 sensor is needed to measure emissions in that environment. The subject is appropriate for Atmospheric Measurement Techniques.
The paper is interesting but there is a lot going on in the short manuscript. What are the objectives of the paper: a modelling study to estimate CH4 enhancement levels downwind of cattle; a gas release trial to determine the enhancement; an examination of DCS sensitivity relative to the cattle signal; an examination of the accuracy of the IDM technique for calculating emissions? The manuscript touches on all of these, but generally without enough description and discussion to address each adequately. The authors likely intend this work as giving a short-overview of the potential of DCS, but it becomes an overly simplified paper that tries to do too much. My main recommendation to the authors is to revise the manuscript to follow a simpler objective(s), so that an expanded explanation and discussion can be added (for the more focused objective). Along these lines:
1. There should be a clear objectives statement in the introduction. There is such a statement deep in the manuscript at line 168: “The main goal of this study is to determine if the DCS can detect small CH4 concentration enhancements downwind from the area of interest, equivalent to those caused by a typical herd of cattle grazing on an extensive pasture.” This is a reasonable objective, but it does not require using the DCS measurements to estimate emissions (using the IDM technique). Other verification trials have shown that IDM can accurately give emissions when provided an accurate concentration measurement. So the authors could drop the emission calculations to simplify the paper.
2. Despite the above comment, the paper is potentially more interesting when the DCS measurements are used to estimate emissions (with IDM). I would be OK making this task the main objective. But the description of the tracer release verification needs improvement (comments below).
3. In terms of dropping material … what is the value of the dispersion model calculations in the section “Sensitivity and precision required for grazing measurements”? If it is to estimate concentration enhancement levels, can these instead be determined from the tracer release results (e.g., Fig 5, 7, 8)? I prefer to see real-world measurements used for this task. Another reason to drop the modelling work is that it is inadequately described (comments below).
Specific Comments
4. Ln 57: “In typical IDM applications, open-path Fourier Transform Infrared (FTIR) sensors are setup upwind and downwind for the source of interest”. I would not refer to use of an FTIR as the “typical” IDM application -- line-averaging lasers (TDLAS) have been more commonly used. I suggest substituting “line-averaging” for “FTIR” here. The association of IDM with “FTIR” also wrongly suggests the McGinn et al. (2011) study used an FTIR (they used a laser).
5. Ln 63: “The limited path distance, bulky apparatus, multi-component retroreflectors (Bai et al., 2022) make employing open-path FTIR challenging in agricultural environments.” Assuming the authors substitute “line-averaging” for “FTIR” as suggested above, they will have to either remove this list of FTIR disadvantages, or introduce the FTIR as a preferred (?) instrument in order to justify including this list.
6. Ln 112: “A forward Lagrangian stochastic model (Windtrax, Thunderbeach Sci.) was used to simulate the concentration field downwind from a hypothetical herd of 20 head of beef cattle grazing in an area of 25 ha …”. Give more details about the simulation: How were the point sources spatially distributed (randomly, evenly, moving)? Where were the laser lines located relative to the sources (e.g., what does 45 m away from the herd mean)? What was the height of the release? How many model particles were used? Clarify these details.
7. Ln 114: “Turbulence data for the simulations were measured … at a grazing unit adjacent to the measurement site. The wind dataset selected …” Explain which wind parameters were used in the simulations. Was the wind direction allowed to vary?
8. Ln 125: “The forward model predicted that a herd of 20 cattle grazing in an area of 25 ha would produce a CH4 enhancement of 16 nmol mol-1 above a 2000 nmol mol-1 background for a beamline 45 m away from the …”. The text implies the concentration enhancements were calculated from 30 days of wind measurements (line 115). Over time the enhancement will change as the wind conditions vary. So what does the single enhancement value of 16 nmol mol-1 represent (average value, median)? What do the enhancement values in Table 1 represent? What about the variability in the enhancement?
9. Ln 162: This paragraph (or a variation of it) would be good in the introduction. It gives important background information and contains a clear objectives statement: “The main goal of this study is to determine if the DCS can detect small CH4 concentration enhancements downwind from the area of interest, equivalent to those caused by a typical herd of cattle grazing on an extensive pasture.”
10. Ln 201: “The CH4 tank was weighted in the beginning and end of the gas release campaigns and the mass of gas released was determined gravimetrically …” For what purpose? To verify the release rate given by the mass flow controller? If so, did this confirm an accurate release rate? Clarify.
11. Ln 220: “The WindTrax input data consisted of … and appropriate wind statistics.” Describe the wind statistics used.
12. Ln 225: “The source area (Fig. 4) used by WindTrax to infer fluxes was set to match the 12.5 m2 area of the CH4 point sources.” Was the area source at ground level?
13. Ln 233: “… where F is the flux, σF is flux error, σ2𝑟𝑑 is downwind (background) mixing ratio error, σ2𝑟𝑢 is upwind mixing ratio error, cov(d,u) is the covariance of the downwind and upwind errors.” Are the σ2 values variances (of what variable)? Reference a good description of Eq. (2).
14. Ln 241: “A CH4 release at a rate of 3078 g day-1 is shown in Fig. 5.” It would be good to remind the reader of the significance of this release rate (e.g., it corresponds to X number of cattle)?
15. Ln 244: “This measurement demonstrates that the DCS system can detect small CH4 enhancements equivalent to the ones caused by a small herd of cattle located at approximately 50 m from the downwind laser beamline.” A) Can this conclusion be justified statistically? B) From Fig. 5 it appears there is no difference between rd and ru for some periods. The magnitude of (rd-ru) will depend on windspeed (i.e., u*), and plotting (rd-ru) vs u* would presumably show the DCS system is not detecting downwind enhancement for higher winds (when there is more dilution of the tracer). This is not a unique problem for DCS as any sensor would show similar trends with u*.
16. Ln 246: “The two-DCS measurement path geometry is also capable of capturing and rejecting the temporal dynamics of the CH4 background driven by changes in atmospheric boundary layer conditions.” What does “rejecting the temporal dynamics” mean?
17. Ln 252. “To determine if any mixing ratio biases exist between the North and South beamlines that may lead to incorrect flux values… The average CH4 flux computed using WindTrax was ± 974 g day-1 equivalent to approximately 5 head of cattle …”. This paragraph is confusing and needs more explanation. A) Remind the reader of what you are doing, e.g., using path concentrations during a period with no gas release in order to determine precision of the DCS+IDM calculation? B) What is the difference between the given uncertainty in the average CH4 flux (+/- 217) and uncertainty using WindTrax (+/- 974)?
18. Ln 258. Figure 6 caption (and Fig. 7, 8). Explain what the error-bars represent … flux uncertainties due to DCS measurement uncertainty (and this does not include IDM uncertainty).
19. Ln 266: “WindTrax computed average CH4 flux was (4002 ± 1498) g day-1, showing a good agreement to the actual release CH4 flux of 3970 g day-1.” A) What does the ± value represent here? B) The emission “recovery” fraction (4002/3970 = 1.008) is phenomenally good. This is worth some commentary and context given other IDM tracer release studies (e.g., a compilation of verification studies is given in the appendix of Harper et al., 2010: The effect of biofuel production on swine farm methane and ammonia emissions. J. Environ. Quality).
20. Ln 290: “The agreement between the computed and actual CH4 fluxes in this study shows that DCS can precisely measure the small concentration enhancements due to a herd of beef cattle in the field at distances up to 100 m from the source area.” A) It would be good to add the caveat about the effect of windspeed on detectability (see comment 15 above). B) I would like the authors to comment on the base level uncertainty (~ 5 cows), e.g., what does this imply about the minimum number of cattle that could be measured?
21. Ln 302: “For example, prior calibrations are often necessary when using multiple FTIR systems to perform multi-path gas concentration measurements.” What is meant by “prior calibrations”? Cross-calibration of different instruments? This is not just an FTIR problem, but a problem with all types of concentration sensors.
22. Line 305: “Expected CH4 horizontal gradients in grazing systems are often small, as demonstrated in this study, so small instrument biases can lead to large errors when inferring fluxes.” Good point.
23. Ln 310: “The driving rationale of this work is to quantify the net CH4 fluxes produced by cattle grazing system …” Are the authors suggesting a role for the DCS system in measuring soil fluxes (by either micrometeorological approaches or chambers)? Is this realistic given the generally small magnitude of the soil fluxes or the logistics of pairing DCS with a chamber.
Citation: https://doi.org/10.5194/egusphere-2024-1181-RC1 -
EC1: 'Editor's comment on egusphere-2024-1181', David Griffith, 09 Jun 2024
Editor review: Using Open-Path Dual-Comb Spectroscopy to Monitor Methane Emissions from Simulated Grazing Cattle
Chinthaka Weerasekara et al AMT
The paper is appropriate for AMT but requires some minor and semi-major revisions before publication. The comments from 2 anonymous referees are valid and should be addressed. In addition I would like to add my editor’s technical comments and corrections below:
L16, also L219 (Eq 1) and many other instances: the usage of “mixing ratio” and “mole fraction” throughout the manuscript is not correct usage. For a mixture of A and B, eg A=CH4 and B=CH4-free air, the mixing ratio is defined as A:B, and the mole fraction is A/(A+B). At 2 ppm levels the difference between the two is small and the names are often interchanged, if incorrectly so. But at higher levels it is significant. Eg the mixing ratio of O2 : air is 21:79 =0.26, the mole fraction of O2 in air is 21/100 = 0.21. In eq 1, if Xch4 is the mole fraction in whole (wet) air, X/(X-Xh2o) is corrected for the variable water content and referred to as the dry air mole fraction. All usage of these terms should be searched, reviewed and corrected throughout the manuscript.
L62. I believe McGinn used TDL instruments not FTIR, please also note anonymous reviewers comment on FTIR vs laser instruments. I agree to use open path as the descriptor, not FTIR.
L76: replace “ideal” with “potentially valuable” – this paper is trying to show this to be true – “ideal” assumes that it is (and you do not need to write this paper...)
L82: reference to Newville et al is not sufficient, the reader should be able to find the reference through a doi or similar.
L96: IGMs (plural) not IGM’s (possessive). Please check for other cases.
Table 1: this would be easier to read with a vertical line after the 1st and 4th columns. Also if CH4 were given as enhancements, at 2000 nmol/mol, not as mole fractions.
L140 - 145 . I have trouble to follow this calculation of SNR and detection limits on several levels – I request that it be completely rewritten.
- L141, what is meant by “normalised” here? Concentration/amount (and to what level), pathlength? What are the units?
- L142 should read (1 – exp(-alpha.L) ) for absorption, the 1 is missing. This equals ~ alpha.L if alpha.L is small
- Optical depth is alpha.L.concentation and dimensionless.
(eg (cm^2 molec^-1).cm.molec cm^-3). What units have you used here? - SNR is calculated in measured intensity or transmission spectra, not in optical depth, which is not linearly related except for weak absorption. They are only the same for weak lines. A given noise level corresponds to a much larger increment of concentration for a line that is already strongly absorbed in the background. It is linedepth:noise that matters for detection limit, not signal:noise
- So I have trouble to interpret the calculation of 5 nmol/mol uncertainty or detection limit, especially in view of the L141 comment above – I don’t know the pathlength or concentration which lead to the 0.03 “normalised” optical depths, and noise should be applied to the transmission spectrum, not optical depth. It makes a big difference if the 0.03 od is for 1 nmol/mol or for 2000 nmol/mol.
- Finally, please state how you define detection limit – commonly this an amount equivalent to 3 x noise in the spectrum.
L147: 12/18/2022 - please avoid this date format, it is ambiguous in an international journal. Although unambiguous in this instance, it is safer to use 18-Dec-2022 or 2022-12-18 format.
L150: Figure 1 should be Figure 3, and 2=> 4. Please check all figure captions, numbers and cross references in the text.
L173-174 … that were used … (not was used)
L183, 185, 189 : (PT100, FLIR etc ) is not sufficient to identify the supplier. Normal usage is model number, manufacturer and location, so they can be followed up.
L219 see L16 comment
L243: Would be better expressed as “Data from a CH4 release ….”. The figure referenc ed is also incorrect on this line.
L254-259. I cannot see how the last sentence in this paragraph relates to what comes before it. If the measured bias from the up-down measurement is 1 +/- 217 g /day using “the IDM”, where does the 974 g/day using Windtrax come from?
Citation: https://doi.org/10.5194/egusphere-2024-1181-EC1 -
RC2: 'Comment on egusphere-2024-1181', Anonymous Referee #2, 10 Jun 2024
The manuscript describes the field deployment of an dual comb spectroscopy (DCS) system along two simultaneously operated open measurement paths with the goal to measure CH4 emissions from cattle. This capability is tested and demonstrated in a controlled release experiment simulating cattle emissions while measuring along approximately 200m long paths upwind and downwind of the release area. The paper describes the experimental setup, hardware, the data processing, and spectral analysis which in the end produce path averaged mixing ratios. It further describes the analysis to infer flux estimations from these mixing ratios and their differences. This work provides a contribution in the ongoing challenge of measuring methane emissions from ruminants and on the open question of how open-path measurements of greenhouse gases (GHGs) are best employed in practice. I recommend publishing this work after addressing some minor and some potential major comments below.
General remarks:
I think you do not clearly and transparently present the extend of your measurement campaign (i.e. line 153 ff.). For what timespan was the DCS setup deployed? How many release experiments did you perform and on which days? The data you present in Figures 5 to 8 spans at least 3 months, yet you do not show for example the emission estimates for the day in Figure 5 which you used to show the enhancements during the release. Did you operate the open-path system during the mentioned grazing period (May to mit July) and try to measure real cattle emissions? If so, what were the additional challenges compared to your controlled release experiment? Since your stated goal is demonstrating the capability of such a system to monitor emissions, I would encourage you to state transparently how often you had high quality results. In its current state, the manuscript generates the impression that the extend of the dataset is intentionally vague and potentially data presented very selectively.Detailed remarks:
Line 23 f.: I do not see how the provided materials show that only optical power limits the measurement. The controlled release experiment had quite accurate knowledge of the release area and the manuscripts does not provide a systematic analysis of the impact of source distribution uncertainties and transport uncertainties, which typically contribute significantly to the uncertainties of fluxes estimated from concentration measurements.
Line 61 f.: To my knowledge, McGinn et al. (2011) did not use an FTIR system.
Line 71: I assume with "square-law photodetector" you mean a photodiode operated in a linear (power to current) regime. If so, calling it that way might make this more accessible to a wider audience. If not, I do not understand the point you are making here.
Line 82 f.: I appreciate you citing LMFIT but think, if you do it, the doi should be included in some form: https://dx.doi.org/10.5281/zenodo.11813
Line 83: You did not cite the most recent version of HITRAN (HITRAN 2020, Gordon et al. 2022). If you did not use the most recent version, you might want to mention which version you used and your reasoning behind that. You also might want to mention which information (i.e. line shape model) you used.
Line 108 f.: I think the concept of "molecular time" would be worth a one line explanation somewhere in the manuscript if you need it.
Line 114: What does "Turbulence data" include? Which parameters were measured and are available for analysis?
Line 138 f.: Your estimation of your measurement sensitivity of 5 nmol/mol is really interesting and I think it would be worth a bit more thorough explanation. I for my part found it challenging to follow you and am still not certain if I understood it correctly.
Line 190: It seems like you did not show or state your exact path lengths anywhere and not in Figure 4.
Line 208: How did you calculate your mole fractions? What did you use to estimate your total airmass?
Line 249: While this is not wrong in any way, I feel that using the combination of arrows and the convention to give the wind direction by where the wind is blowing from generates an unnecessary potential for misunderstanding.
Line 254 f.: I do not understand the difference between your estimate of 1 +- 217 g/day using the IDM and your WindTrax estimation of +-974 g/day. I thought, that WindTrax is your IDM (Line 220).
Figure 8 b-d: I would again encourage you to show a bit more context around the presented data. If you have the data from 13:00 to 14:00 available, your plots here would provide a better impression on how well the signal can be distinguished from the background.
Line 291 ff.: As in Line 23 f. I do not see where you presented data on the transmitted power over time and how this is limiting your performance.
Line 298 ff.: Again, I do not see where you discussed the technical aspects you list in point 1).
Line 335: You might want to mention how contributed to drafting/writing up the manuscript.Citation: https://doi.org/10.5194/egusphere-2024-1181-RC2
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