TY - JOUR
T1 - Evaluating the use of an Unmanned Aerial Vehicle (UAV)-based active AirCore system to quantify methane emissions from dairy cows
AU - Vinković, Katarina
AU - Andersen, Truls
AU - de Vries, Marcel
AU - Kers, Bert
AU - van Heuven, Steven
AU - Peters, Wouter
AU - Hensen, Arjan
AU - van den Bulk, Pim
AU - Chen, Huilin
PY - 2022/7/20
Y1 - 2022/7/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127491775&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.154898
DO - 10.1016/j.scitotenv.2022.154898
M3 - Article
SN - 0048-9697
VL - 831
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 154898
ER -