EGU26-9756, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-9756
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Thursday, 07 May, 10:45–12:30 (CEST), Display time Thursday, 07 May, 08:30–12:30
 
Hall X5, X5.132
Quantifying Farm-Scale Methane Emissions using Downwind Gaussian Plume Modelling with Tracer Correlation
Mackenzie LeVernois1, James France1,2, Victoria Rafflin1, Dave Lowry1, Nigel Yarrow3, Jacob Shaw3, Fabrizio Innocenti3, Jon Helmore3, Mathias Lanoisellé1, Aliah Al-Shalan1, and Rebecca Fisher1
Mackenzie LeVernois et al.
  • 1Royal Holloway, University of London, Earth Sciences, Egham, United Kingdom
  • 2Environmental Defence Fund Europe, Brussels, Belgium
  • 3National Physical Laboratory, Teddington, United Kingdom

Agricultural methane emissions, from enteric fermentation and manure management, account for approximately 32% of global anthropogenic methane emissions, yet standardized farm- or herd-scale quantification methods remain lacking (Nisbet et al., 2025). Downwind mobile surveys using Gaussian plume modelling for point sources, primarily from oil and gas, and tracer dispersion methods for diffused sources, such as landfills, have been demonstrated to be effective, relatively low-uncertainty approaches for quantifying methane emissions.

Here, we present a computationally efficient framework for quantifying farm-scale methane emissions using Gaussian plume modelling, developed for small- to medium- scale farms in the UK, with ongoing work to extend the approach to grazing cattle. Using a Lagrangian particle model (Oettl & Kuntner, 2024), a virtual upwind point source is assigned to encompass the farm emission footprint (De Visscher, 2014). Gaussian dispersion modelling with Monte Carlo iterations is then applied to downwind vehicle-based methane measurements to derive farm-scale emission estimates.

These estimates are evaluated against results from a simultaneous controlled tracer release conducted by the National Physical Laboratory at the same farm site. We discuss associated uncertainties and highlight future improvements, including process automation, source apportionment, and methods for quantifying emissions from grazing herds.

 

References:

De Visscher, A. (2014). Air Dispersion Modeling: Foundations and Applications. Wiley. https://doi.org/10.1002/9781118723098

Dietmar Oettl & Markus Kuntner. (2024). GRAL: Graz Lagrangian Model (Version 24.11) [Computer software]. Graz University of Technology. https://gral.tugraz.at/

Nisbet, E. G., Manning, M. R., Lowry, D., Fisher, R. E., Lan, X. (Lindsay), Michel, S. E., France, J. L., Nisbet, R. E. R., Bakkaloglu, S., Leitner, S. M., Brooke, C., Röckmann, T., Allen, G., Denier van der Gon, H. A. C., Merbold, L., Scheutz, C., Woolley Maisch, C., Nisbet-Jones, P. B. R., Alshalan, A., … Dlugokencky, E. J. (2025). Practical paths towards quantifying and mitigating agricultural methane emissions. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 481(2309), 20240390. https://doi.org/10.1098/rspa.2024.0390

How to cite: LeVernois, M., France, J., Rafflin, V., Lowry, D., Yarrow, N., Shaw, J., Innocenti, F., Helmore, J., Lanoisellé, M., Al-Shalan, A., and Fisher, R.: Quantifying Farm-Scale Methane Emissions using Downwind Gaussian Plume Modelling with Tracer Correlation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9756, https://doi.org/10.5194/egusphere-egu26-9756, 2026.