Field Measurements of Spatial Air Emissions from Dairy Pastures Using an Unmanned Aircraft System

Abstract

Unmanned aircraft systems (UASs) are emerging as useful tools in environmental studies due to their mobility and ability to cover large areas. In this study, we used an air analyzer attached to a UAS to measure gas and particulate matter (PM) emissions from rotationally grazed dairy pastures in northern Wisconsin. UAS-based sampling enabled wireless data transmission using the LoRa protocol to a ground station, synchronizing with a cloud server. During the measurements, latitude, longitude, and altitude were recorded using a high-precision global positioning system (GPS). Over 1200 measurements per parameter were made during each site visit. The spatial distribution of the emission rates was estimated using the Lagrangian mass balance approach and Kriging interpolation. A horizontal sampling probe effectively minimized the impact of propeller downwash on the measurements. The average concentrations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were 800.1 ± 39.7 mg m−3, 1.38 ± 0.063 mg m−3, and 0.71 ± 0.03 mg m−3, respectively. No significant difference was found between CO2 concentrations measured by the UAS sensor and gas chromatography (p = 0.061). Emission maps highlighted variability across the pasture, with an average CO2 emission rate of 1.52 ± 0.80 g day−1 m−2, which was within the range reported in the literature. Future studies could explore the impact of pasture management on air emissions.

Keywords

• drone
• greenhouse gas
• Kriging interpolation
• LoRa protocol
• particulate matter
• pasture