Livestock production is a relevant anthropogenic source of gaseous and particulate pollutants. The increasing regulatory pressure to reduce emissions requires their systematic assessment. However, current methodologies for accurate GHGs, ammonia and particulate measurements at farm level demand extensive field and laboratory work, with high costs in terms of equipment and skilled personnel. In this context, the development of cost-effective methods for rapid and systematic monitoring of emissions is a key element. A UAV-based system was developed to measure gas (CO2, CH4, NH3) and particulate matter (PM2.5, PM10) concentrations in the bottom atmospheric boundary layer. The system is founded on a flexible architecture and can be adapted to different operating environments. Prototype measuring units equipped with low-cost sensors were designed and implemented with the aim to identify emission hotspots. The units are designed to be employed both for ground measurements and for in-flight data collection on board of a customised UAV. Two flight missions were carried out in a dairy farm to evaluate the feasibility of ground and in-flight measurements. Ground units were positioned both inside and outside the building where dairy cows were housed, while simultaneous measurements were collected by the UAV. The results obtained showed that the prototype units are able to provide ground and in-flight measures of gases and PM, however further research is required to embed additional sensors and validate data across multiple state of the art methods.

A UAV-based system for greenhouse gases and particulate measurement in livestock farms / Valentina Becciolini, Leonardo Conti, Giuseppe Rossi, Marco Merlini, Gabriele Coletti, Ugo Rossi, Matteo Barbari. - ELETTRONICO. - (2022), pp. 450-456. ((Intervento presentato al convegno 10th European Conference on Precision Livestock Farming, ECPLF 2022 tenutosi a Vienna (Austria) nel 29 agosto - 1 settembre 2022.

A UAV-based system for greenhouse gases and particulate measurement in livestock farms

Valentina Becciolini;Leonardo Conti;Giuseppe Rossi
;
Marco Merlini;Matteo Barbari
2022

Abstract

Livestock production is a relevant anthropogenic source of gaseous and particulate pollutants. The increasing regulatory pressure to reduce emissions requires their systematic assessment. However, current methodologies for accurate GHGs, ammonia and particulate measurements at farm level demand extensive field and laboratory work, with high costs in terms of equipment and skilled personnel. In this context, the development of cost-effective methods for rapid and systematic monitoring of emissions is a key element. A UAV-based system was developed to measure gas (CO2, CH4, NH3) and particulate matter (PM2.5, PM10) concentrations in the bottom atmospheric boundary layer. The system is founded on a flexible architecture and can be adapted to different operating environments. Prototype measuring units equipped with low-cost sensors were designed and implemented with the aim to identify emission hotspots. The units are designed to be employed both for ground measurements and for in-flight data collection on board of a customised UAV. Two flight missions were carried out in a dairy farm to evaluate the feasibility of ground and in-flight measurements. Ground units were positioned both inside and outside the building where dairy cows were housed, while simultaneous measurements were collected by the UAV. The results obtained showed that the prototype units are able to provide ground and in-flight measures of gases and PM, however further research is required to embed additional sensors and validate data across multiple state of the art methods.
Precision livestock farming ‘22
10th European Conference on Precision Livestock Farming, ECPLF 2022
Vienna (Austria)
29 agosto - 1 settembre 2022
Valentina Becciolini, Leonardo Conti, Giuseppe Rossi, Marco Merlini, Gabriele Coletti, Ugo Rossi, Matteo Barbari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1285952
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