A UAV-Based System for Validating a Backward Lagrangian Stochastic Model in a Dairy Cattle Farm
Alessio Mattia, Valentina Becciolini, Leonardo Conti, Marco Merlini, Patricia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Jacqueline Cardoso Ferreira, Franck Morais de Oliveira, Giuseppe Rossi
TL;DR
This study uses a drone system to test a model for CO2 emissions from a dairy farm compost barn under stable weather conditions.
Contribution
The first validation of a backward Lagrangian stochastic model at two altitudes using a UAV-based system in a compost-bedded pack barn.
Findings
The bLS model overestimated CO2 concentrations by 8–39% at 5.0 m and 13–21% at 10.0 m above ground level.
Sensitivity analysis showed emission flux differences between stable and unstable atmospheric conditions ranging from −0.020 to 0.034 g ∙ m−2 ∙ s−1.
Root mean square error indicated overestimates of CO2 concentrations from 44 to 275 ppm.
Abstract
This study characterizes a compost-bedded pack barn of a dairy cattle farm in terms of CO2 emissions approximately 20 min after tilling under stable atmospheric conditions. Emission fluxes were calculated with the bLS model WindTrax, assessing modeled CO2 concentrations at two altitudes (5.0 m and 10.0 m ABGL) by comparing them with those measured by a UAV-based system at the same two altitudes. The UAV-based system was equipped with a low-cost self-engineered MSP (multi-sensor platform) containing an NDIR sensor for measuring concentrations and detecting environmental conditions, which were measured both by MSPs and commercial sensors. The input data were provided by the same sensors positioned on the ground (1.5 m ABGL), upwind and downwind with respect to the emission source. A sensitivity analysis of atmospheric stability in the bLS model yielded differences between median…
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Taxonomy
TopicsOdor and Emission Control Technologies · Indoor Air Quality and Microbial Exposure · Effects of Environmental Stressors on Livestock