XXXVIII ciclo Alessio Mattia
2nd year
Curriculum: Agricultural and Forest Engineering
Supervisor: Prof. Giuseppe Rossi
Email: alessio.mattia @unifi.it
Office address: Via San Bonaventura 13, 50145 Firenze
Mobile phone: +39 3281965198
Profile
Alessio Mattia, born in Prato (PO), Italy in 04/05/1996, holds a Bachelor’s degree in Agricultural Sciences in 2020 and a Master’s degree in Agricultural sciences and Technologies (curriculum: Planning and Management for Agro-territorial Biosystems) in 2022 at the University of Florence.
Research interests and PhD project
His main Research interests focus on precision agriculture, monitoring and representation of air pollutants emissions from crop and livestock farms. His doctoral project contributes to a decision support system, including using unmanned aerial vehicle, multisensor platforms and various type of software to represent emissions in farm. After quantifying emissions, this system helps farmers to implement mitigation strategies or best available technologies considering life cycle assessment methodology.
Papers
- Finzi, A., Mautone, A., Becciolini, V., Mattia, A., Rossi, G., Conti, L., Vitaliano, S., D’Urso, P., Arcidiacono, C., 2024. Emission-controlled intensive livestock housing systems for ecological transition: innovative measuring, mitigating and mapping strategies (EMILI). In AIIA Padova 2024 Mid-Term Conference Book of abstract (pp. 3-3). AIIA, Associazione Italiana di Ingegneria Agraria. https://aiiapadova2024.com/instruction-for-reviewers/
- Mattia, A., Merlini, M., Pinheiro Amantea, R., Coletti, G., Squillace, F., Rossi, G., Barbari, M., Becciolini, V., 2023. Deducing emission rates from gas concentrations in a livestock farm through a backward Lagrangian stochastic method-based model. In AISSA# under40 Book of abstract (pp. 120-120). AISSA, Associazione Italiana delle Società Scientifiche Agrarie. https://flore.unifi.it/handle/2158/1356506
Conference talks and seminars
- Poster at The XX CIGR World Congress 2022 - Sustainable Agricultural Production. Mapping atmospheric concentration of gas and particulate matter in livestock farms using drone-based measurements.https://flore.unifi.it/handle/2158/1293839
Ultimo aggiornamento
11.10.2024