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Enteric fermentation and manure methane emissions from livestock are major anthropogenic greenhouse gas emissions. In general, direct measurements of farm-scale methane emissions are scarce due to the source complexity and the limitations of existing atmospheric sampling methods. Using an innovative UAV-based active AirCore system, we have performed accurate atmospheric measurements of CH4 mole fractions downwind of a dairy cow farm in the Netherlands on four individual days during the period from March 2017 to March 2019. The total CH4 emission rates from the farm were determined using the UAV-based mass balance approach to be 1.1-2.4 g/s. After subtracting estimated emission factors of manure onsite, we derived the enteric emission factors to be 0.20-0.51 kgCH4/AU/d (1 AU = 500 kg animal weight) of dairy cows. We show that the uncertainties of the estimates were dominated by the variabilities in the wind speed and the angle between the wind and the flight transect. Furthermore, nonsimultaneous sampling in the vertical direction of the plume is one of the main limiting factors to achieving accurate estimate of the CH4 emissions from the farm. In addition, a N2O tracer release experiment at the farm was performed when both a UAV and a mobile van were present to simultaneously sample the N2O tracer and the CH4 plumes from the farm, improving the source quantification with a correction factor of 1.04 and 1.22 for the inverse Gaussian approach and for the mass balance approach, respectively. The UAV-based active AirCore system is capable of providing useful estimates of CH4 emissions from dairy cow farms. The uncertainties of the estimates can be improved when combined with accurate measurements of local wind speed and direction or when combined with a tracer approach.

Despite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH4), δ13C-CH4, and δD-CH4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ13C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH4) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ13C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions.