| 01 Sep 2025
Dynamic quantification of methane emissions at facility scale using laser tomography: demonstration of a farm deployment
Abstract. Detecting and quantifying greenhouse gas (GHG) emissions is essential for understanding global GHG budgets, updating emission inventories, and evaluating climate change mitigation efforts. Most anthropogenic emissions occur at the scale of facilities, and emission distribution in time and space relates to facility operations. This paper focuses on a novel GHG monitoring technique referred to as laser dispersion tomography (LDT). It uses sequential multi-beam open-path laser dispersion spectroscopy measurements and wind data to infer dynamic GHG concentration and source maps at facility scale. In this work, the use of LDT for monitoring methane emissions in agriculture is demonstrated by deploying it on an operational farm. For this aim, computational methods used in data analysis of LDT are also further developed. Particularly, we introduce spatial constraints to the tomographic reconstruction based on prior knowledge on potential source locations – information often available in facility-scale GHG monitoring applications – and investigate numerically whether such constraints could improve the tolerance of LDT to effects of conditions typical in farm environments, such as complex wind fields caused by buildings and interfering external emission sources. The results of numerical studies indicate that including spatial constraints reduces the uncertainty and improves the reliability of source quantification in such conditions. In the experimental study, dynamic emission patterns caused by various operations in the farm, such as slurry and dry manure management, are well captured, both temporally and spatially. The results support the feasibility of LDT as a tool for robust quantification of GHG mass emission rates at farms, especially when the spatial constraining of sources is possible. Owing to the fine spatial and temporal resolution of LDT, we foresee its use in improving GHG emission inventories through fine parametrization, and also its extension to other GHGs and other sectors contributing to global emissions.
The manuscript showcases the time resolved measurement of methane emissions from a dairy farm (on the facility level, with a potential for spatial attribution). It describes the full process, starting with a brief description of the optical technique itself, continuing with the modelling of the atmospheric transport and arriving at the inversion framework. The authors do a great job at clearly separating the setup specific part of the model (observation model) and the general part (evolution model) which improves the accessibility and relevance for different communities (in-situ, remote sensing, etc.). The manuscript goes on to include synthetic studies as well as demonstrations with real data. The study is quite extensive and an uncommon example of combining all the above in a single manuscript. Since the topic of inferring greenhouse gas emissions from concentration measurements in general and from path-average concentration measurements in particular is highly relevant to this journal, I recommend publishing the manuscript after addressing some minor issues. Is summed those up in two general points (concerning the literature overview and the general research question) and multiple line-by-line comments.
Literature overview:
I feel that your overview of the spectroscopic methods is too limited. It ignores at least the following techniques:
NIR long open-path FTIR techniques [1-2], which do not suffer from the limited range as MIR FTIR due to higher brightness.
Dual Comb Spectroscopy (DCS), which was even already employed for different emission estimations, including from cattle [3-4]
Other methods utilizing frequency combs or super-continuum sources [5]
There is also a wider context of tomography using path averaged measurements of gas concentration retrieved from optical measurements going back more than two decades, which would be fair to mention here somehow. Difficult to pick a single one, but for example [6].
Finally, it seems reasonable to mention other projects with similar methodology, even if the trace gas under analysis or the scale differs. For example [7].
General research question:
Likely as a result of the manuscript being so extensive and addressing so many facets, I feel that at multiple points the concrete research question is a bit unclear. I try to point some of these situations out in my line-by-line comments below. I encourage the authors to try to phrase their research questions precisely and go over the manuscript again, removing details where it is maybe not necessary and adding where the main message needs it.
Line-by-line comments:
References:
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[2] Deutscher, N. M., Naylor, T. A., Caldow, C. G. R., McDougall, H. L., Carter, A. G., and Griffith, D. W. T.: Performance of an open-path near-infrared measurement system for measurements of CO2 and CH4 during extended field trials, Atmos. Meas. Tech., 14, 3119–3130, https://doi.org/10.5194/amt-14-3119-2021, 2021.
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[4] Weerasekara, C., Morris, L. C., Malarich, N. A., Giorgetta, F. R., Herman, D. I., Cossel, K. C., Newbury, N. R., Owensby, C. E., Welch, S. M., Blaga, C., DePaola, B. D., Coddington, I., Washburn, B. R., and Santos, E. A.: Using open-path dual-comb spectroscopy to monitor methane emissions from simulated grazing cattle, Atmos. Meas. Tech., 17, 6107–6117, https://doi.org/10.5194/amt-17-6107-2024, 2024.
[5] Roderik Krebbers, Kees van Kempen, Yueyu Lin, Joris Meurs, Lisanne Hendriks, Ralf Aben, José R. Paranaiba, Christian Fritz, Annelies J. Veraart, Amir Khodabakhsh, Simona M. Cristescu, Ultra-broadband coherent open-path spectroscopy for multi-gas monitoring in wastewater treatment, Environmental Science and Ecotechnology, Volume 25, 2025, https://doi.org/10.1016/j.ese.2025.100554. (https://www.sciencedirect.com/science/article/pii/S2666498425000328)
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[7] Lian, J., Bréon, F.-M., Broquet, G., Zaccheo, T. S., Dobler, J., Ramonet, M., Staufer, J., Santaren, D., Xueref-Remy, I., and Ciais, P.: Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris from the GreenLITE™ laser imaging experiment, Atmos. Chem. Phys., 19, 13809–13825, https://doi.org/10.5194/acp-19-13809-2019, 2019.
[8] M Knapp et al 2023 Environ. Res. Lett. 18 044030, DOI: 10.1088/1748-9326/acc346
[9] Brunner, D., Suter, I., Bernet, L., Constantin, L., Grange, S. K., Rubli, P., Li, J., Chen, J., Bigi, A., and Emmenegger, L.: Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL, Atmos. Chem. Phys., 25, 14387–14410, https://doi.org/10.5194/acp-25-14387-2025, 2025.