Andmebaasi logo

Sirvi Autor "Mattia, A." järgi

Nüüd näidatakse 1 - 2 2
  • Pisipilt
    Kirje
    Assessment of CO2 emission rate from extended area sources with WindTrax model in a dairy cattle farm
    (Estonian University of Life Sciences, 2025) Mattia, A.; Merlini, M.; Squillace, F.; Rossi, G.; Conti, L.; Becciolini, V.
    This paper aimed to evaluate the WindTrax model to quantify CO2 (carbon dioxide) emissions in a commercial dairy cattle farm in Central Italy with a low-cost measurement system. A field trial of 20 minutes was conducted in February 2023, using two G-eko 2.0 MSPs (multi-sensor platforms), an anemometer, and a GNSS receiver, in unstable atmospheric conditions. Then, 5-minute averaged data were used as input in the WindTrax software for applying the backward Lagrangian Stochastic model. The model was used for calculating four mean CO2 emission rates (0.20212 ± 0.04994 g m-2 s-1) with 50,000 particles and the horizontal dispersion of CO2 concentrations around the sources using different numbers of particles (5,000, 10,000, 30,0000, and 50,000). Atmospheric dispersion maps, confidence interval concentration maps, and vertical profile plots were obtained by increasing the number of particles. The model shows better performances, in terms of confidence intervals, with a high number of particles with a stabilization of modeled median values between 30,000 and 50,000 particles. Horizontally, the lowest confidence intervals (near to zero) were obtained at 100–150 m from the sources along the wind direction, suggesting that the downwind sensor could be placed at a greater distance. Similarly, a better-defined vertical trend in modeled concentrations is observed as the number of particles increases. Wind gusts could have a great effect on emission rate calculation with limited sampling periods, as in this case, but simultaneously unstable atmospheric conditions affect the increased dispersion and dilution of CO2.
  • Pisipilt
    Kirje
    Remotely piloted aircraft for monitoring greenhouse gases in dairy production systems
    (Estonian University of Life Sciences, 2025) Aguiar, J.V.; Ferraz, P.F.P.; Ferraz, G.A.S.; Ferreira, J.C.; Cecchin, D.; Mattia, A.; Conti, L.; Rossi, G.
    The monitoring of greenhouse gas (GHG) emissions in dairy cattle facilities is essential for understanding and mitigating the environmental impact of livestock farming. Among the main gases emitted in dairy production systems, methane (CH4) and carbon dioxide (CO2) play significant roles in global warming. The objective of this research was to evaluate the spatial variability of CH4 (ppm) and CO2 (ppm) concentrations, as well as environmental variables (dry bulb temperature, tdb, °C, and relative humidity, RH, %), in a compost barn dairy production system. For gas concentration monitoring, an electrochemical sensor was used for CH4 and a non–dispersive infrared (NDIR) sensor for CO2. For the environmental variables, a Hobo® MX2301A datalogger was used, and both pieces of equipment were attached to a remotely piloted aircraft (RPA), the DJI Matrice 350. Measurements were carried out over three days, with flights conducted over the facility's roof. The data obtained were analysed using geostatistics to characterise spatial variability of the GHG. A strong spatial dependence was observed in gas concentrations and environmental variables. The highest concentrations of CH4 (129–134.4 ppm) and CO2 (434–479 ppm) were recorded on the first day. Tdb ranged between 24.2 °C and 32 °C, while RH fluctuated between 38.8% and 68%. The use of RPA proved to be an efficient tool for GHG monitoring, allowing the identification of spatial distribution patterns. This technology provides a novel approach to measuring GHG emissions, addressing the environmental challenges of the agricultural sector.