{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T06:05:35Z","timestamp":1783490735084,"version":"3.55.0"},"reference-count":81,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 calculated emission fluxes for stable and unstable conditions from \u22120.020 to 0.034 g \u2219 m\u22122 \u2219 s\u22121. Mean percentage errors gave overestimates of 8\u201339% and 13\u201321% 5.0 m and 10.0 m ABGL. The RMSE also indicated overestimates ranging from 44 to 275 ppm. This is the first study to validate concentrations calculated by a bLS model at two altitudes by using a UAV-based system on a compost-bedded pack barn.<\/jats:p>","DOI":"10.3390\/s25216733","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:21:46Z","timestamp":1762194106000},"page":"6733","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A UAV-Based System for Validating a Backward Lagrangian Stochastic Model in a Dairy Cattle Farm"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1700-2753","authenticated-orcid":false,"given":"Alessio","family":"Mattia","sequence":"first","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, Via di San Bonaventura 13, 50145 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1344-4634","authenticated-orcid":false,"given":"Valentina","family":"Becciolini","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, Via di San Bonaventura 13, 50145 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4181-5893","authenticated-orcid":false,"given":"Leonardo","family":"Conti","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, Via di San Bonaventura 13, 50145 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7284-6198","authenticated-orcid":false,"given":"Marco","family":"Merlini","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, Via di San Bonaventura 13, 50145 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9708-0259","authenticated-orcid":false,"given":"Patricia Ferreira Ponciano","family":"Ferraz","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-2210","authenticated-orcid":false,"given":"Gabriel Ara\u00fajo e Silva","family":"Ferraz","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6983-4634","authenticated-orcid":false,"given":"Jacqueline Cardoso","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6322-6424","authenticated-orcid":false,"given":"Franck Morais","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-9294","authenticated-orcid":false,"given":"Giuseppe","family":"Rossi","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food, Environment and Forestry, University of Florence, Via di San Bonaventura 13, 50145 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2024). World Population Prospects 2024: Summary of Results, UN Department of Economic and Social Affairs. DESA\/POP\/2024\/TR\/NO. 9."},{"key":"ref_2","unstructured":"(2025, May 20). The 17 Goals. Available online: https:\/\/sdgs.un.org\/goals."},{"key":"ref_3","unstructured":"(2025, May 20). Ambient (Outdoor) Air Pollution. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ambient-(outdoor)-air-quality-and-health."},{"key":"ref_4","unstructured":"Shukla, P.R., Skea, J., Reisinger, A., Slade, R., Fradera, R., Pathak, M., Al Khourdajie, A., Belkacemi, M., Van Diemen, R., and Hasija, A. (2023). Summary for Policymakers. Climate Change 2022\u2014Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_5","unstructured":"FAO (2022). GLEAM 3 Dashboard. Shiny Apps, Available online: https:\/\/foodandagricultureorganization.shinyapps.io\/GLEAMV3_Public\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.chemosphere.2015.08.028","article-title":"Development of a multicopter-carried whole air sampling apparatus and its applications in environmental studies","volume":"144","author":"Chang","year":"2016","journal-title":"Chemosphere"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Samad, A., Florez, D.A., Chourdakis, I., and Vogt, U. (2022). Concept of Using an Unmanned Aerial Vehicle (UAV) for 3D Investigation of Air Quality in the Atmosphere-Example of Measurements Near a Roadside. Atmosphere, 13.","DOI":"10.3390\/atmos13050663"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Baglioli, F., and Godoi, R.H. (2023). Sniffing Drones: A Promising Solution for Measuring Railroad Emissions in Urban Environments. Atmosphere, 14.","DOI":"10.3390\/atmos14050865"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4546","DOI":"10.1021\/acs.est.5b06068","article-title":"Comparing Natural Gas Leakage Detection Technologies Using an Open-Source \u201cVirtual Gas Field\u201d Simulator","volume":"50","author":"Kemp","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112419","DOI":"10.1016\/j.envres.2021.112419","article-title":"Offshore field experiments with in-situ burning of oil: Emissions and burn efficiency","volume":"205","author":"Faksness","year":"2022","journal-title":"Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19584","DOI":"10.1021\/acs.est.4c02069","article-title":"Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States","volume":"58","author":"Delorey","year":"2024","journal-title":"Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.envpol.2018.04.100","article-title":"Vertical profiles of lung deposited surface area concentration of particulate matter measured with a drone in a street canyon","volume":"241","author":"Kuuluvainen","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Beryozkina, S., and Al-Shakhs, N. (2020, January 9\u201312). Real-life Application of the Emission Monitoring System by Using a Drone. Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Madrid, Spain.","DOI":"10.1109\/EEEIC\/ICPSEurope49358.2020.9160738"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, S.-H., and Kwak, K.-H. (2020). Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17186915"},{"key":"ref_15","first-page":"191","article-title":"The Concept of a Collection System for Gas Mixture from the Interior of Chimney Openings for Unmanned Flying Systems","volume":"15","author":"Klimczyk","year":"2021","journal-title":"Adv. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"134165","DOI":"10.1016\/j.chemosphere.2022.134165","article-title":"A study of the vertical homogeneity of trace gases in East Asian continental outflow","volume":"297","author":"Chang","year":"2022","journal-title":"Chemosphere"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"158974","DOI":"10.1016\/j.scitotenv.2022.158974","article-title":"Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone","volume":"856","author":"Kuruppa","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"194","DOI":"10.35208\/ert.1369716","article-title":"Smart environmental drone utilization for monitoring urban air quality","volume":"7","author":"Durgun","year":"2024","journal-title":"Environ. Res. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"121147","DOI":"10.1016\/j.atmosenv.2025.121147","article-title":"An integrated drone-based platform with novel correction methods for accurate vertical profiling of black carbon","volume":"349","author":"Lee","year":"2025","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157290","DOI":"10.1016\/j.scitotenv.2022.157290","article-title":"Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system","volume":"846","author":"Esclapez","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Angrisani, L., Amodio, A., Arpaia, P., Asciolla, M., Bellizzi, A., Bonavolont\u00e0, F., Carbone, R., Caputo, E., Karamanolis, G., and Martire, V. (2019, January 24\u201326). An Innovative Air Quality Monitoring System based on Drone and IoT Enabling Technologies. Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy.","DOI":"10.1109\/MetroAgriFor.2019.8909245"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.5194\/amt-15-2177-2022","article-title":"Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources","volume":"15","author":"Morales","year":"2022","journal-title":"Atmos. Meas. Tech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, T., Yang, D., Liu, Y., Cai, Z., Yao, L., Che, K., Ren, X., Bi, Y., Yi, Y., and Wang, J. (2022). Development of an Integrated Lightweight Multi-Rotor UAV Payload for Atmospheric Carbon Dioxide Mole Fraction Measurements. Atmosphere, 13.","DOI":"10.3390\/atmos13060855"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119476","DOI":"10.1016\/j.atmosenv.2022.119476","article-title":"Use of a drone-based sensor as a field-ready technique for short-term concentration mapping of air pollutants: A modeling study","volume":"294","author":"Wu","year":"2023","journal-title":"Atmos. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tiwari, G., and Lall, B. (2023, January 16\u201318). Low-Cost Sensors Based PM Profiling Using Aerial Platform. Proceedings of the 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), Rajkot, India.","DOI":"10.1109\/R10-HTC57504.2023.10461737"},{"key":"ref_26","unstructured":"Bernabeo, A.R., D\u2019Alessandro, G., Ceruti, A., Tositti, L., Nguyen, N., and Ho, T.P. (2023, January 10\u201313). Air quality monitoring using drones (UAV). Proceedings of the International Conference on Sustainable Energy and Green Technology 2023, Ho Chi Minh City, Vietnam."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ranganathan, R.H., Balusamy, S., Partheeban, P., Mani, C., Sridhar, M., and Rajasekaran, V. (2023). Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology. Sustainability, 15.","DOI":"10.3390\/su151813347"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tosato, P., Facinelli, D., Prada, M., Gemma, L., Rossi, M., and Brunelli, D. (2019, January 10\u201312). An autonomous swarm of drones for industrial gas sensing applications. Proceedings of the 2019 IEEE 20th International Symposium on \u201cA World of Wireless, Mobile and Multimedia Networks\u201d (WoWMoM), Washinton, DC, USA.","DOI":"10.1109\/WoWMoM.2019.8793043"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"118193","DOI":"10.1016\/j.atmosenv.2021.118193","article-title":"Wildland fire emission sampling at Fishlake National Forest, Utah using an unmanned aircraft system","volume":"247","author":"Aurell","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.5194\/amt-11-2441-2018","article-title":"Implementation of electrochemical, optical and denuder-based sensors and sampling techniques on UAV for volcanic gas measurements: Examples from Masaya, Turrialba and Stromboli volcanoes","volume":"11","author":"Tirpitz","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1029\/2018GC007692","article-title":"Dynamics of Outgassing and Plume Transport Revealed by Proximal Unmanned Aerial System (UAS) Measurements at Volc\u00e1n Villarrica, Chile","volume":"20","author":"Liu","year":"2019","journal-title":"Geochem. Geophys. Geosys."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Habib, M.R., Baticados, E.N., and Capareda, S.C. (2022). Particulate Matter Emission Factors for Dairy Facilities and Cattle Feedlots during Summertime in Texas. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph192114090"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1007\/s10661-024-12725-9","article-title":"Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables","volume":"196","author":"Velez","year":"2024","journal-title":"Environ. Monit. Assess."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3334","DOI":"10.3390\/s150203334","article-title":"Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses","volume":"15","author":"Joossen","year":"2015","journal-title":"Sensors"},{"key":"ref_35","first-page":"159","article-title":"The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV","volume":"5","author":"Zheng","year":"2018","journal-title":"Front. Agric. Sci. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Flem, A.A., Ghirardelli, M., Kral, S.T., Cheynet, E., Kristensen, T.O., and Reuder, J. (2024). Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV. Atmosphere, 15.","DOI":"10.20944\/preprints202401.0756.v1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Altamira-Colado, E., Cuevas-Gonz\u00e1lez, D., Reyna, M.A., Garc\u00eda-V\u00e1zquez, J.P., Avitia, R.L., and Osornio-Vargas, A.R. (2024). Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review. Atmosphere, 15.","DOI":"10.3390\/atmos15050515"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jo\u0144ca, J., Pawnuk, M., Bezyk, Y., Arsen, A., and S\u00f3wka, I. (2022). Drone-Assisted Monitoring of Atmospheric Pollution-A Comprehensive Review. Sustainability, 14.","DOI":"10.3390\/su141811516"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kim, W.Y., Lee, S.G., Lee, H., and Ahn, K.-H. (2025). Investigation of Vertical Profiles of Particulate Matter and Meteorological Variables up to 2.5 km in Altitude Using a Drone-Based Monitoring System. Atmosphere, 16.","DOI":"10.3390\/atmos16010093"},{"key":"ref_40","first-page":"513","article-title":"Estimating Tracer Emissions with a Backward Lagrangian Stochastic Technique","volume":"Volume 47","author":"Hatfield","year":"2005","journal-title":"Micrometeorology in Agricultural Systems"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1175\/1520-0450(1995)034<1320:BTLSDM>2.0.CO;2","article-title":"Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions","volume":"34","author":"Flesch","year":"1995","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_42","unstructured":"Crenna, B.P. (2025, May 20). WindTrax, Available online: http:\/\/www.thunderbeachscientific.com\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.atmosenv.2014.06.013","article-title":"Use of open-path FTIR and inverse dispersion technique to quantify gaseous nitrogen loss from an intensive vegetable production site","volume":"94","author":"Bai","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lavrsen Kure, J., Krabben, J., Pedersen, S.V., Carozzi, M., and Sommer, S.G. (2018). An Assessment of Low-Cost Techniques to Measure Ammonia Emission from Multi-Plots: A Case Study with Urea Fertilization. Agronomy, 8.","DOI":"10.3390\/agronomy8110245"},{"key":"ref_45","first-page":"1187","article-title":"Evaluation of ammonia emissions from filtration of digestate used for fertigation","volume":"52","author":"Finzi","year":"2021","journal-title":"J. Agric. Eng."},{"key":"ref_46","first-page":"1","article-title":"Applicazione della modellistica inversa per la stima del flusso emissivo di ammoniaca in ambito zootecnico","volume":"9","author":"Angelino","year":"2022","journal-title":"Ing. dell\u2019Ambiente"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.wasman.2016.06.002","article-title":"Ammonia emissions from an anaerobic digestion plant estimated using atmospheric measurements and dispersion modelling","volume":"56","author":"Bell","year":"2016","journal-title":"Waste Manag."},{"key":"ref_48","first-page":"100131","article-title":"Comparison of forward and backward Lagrangian transport modelling to determine methane emissions from anaerobic digestion facilities","volume":"12","author":"Hrad","year":"2021","journal-title":"Atmos. Environ. X"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.13031\/trans.12646","article-title":"Ammonia Emissions from Dairy Lagoons in the Western U.S","volume":"61","author":"Leytem","year":"2018","journal-title":"Trans. ASABE"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.agrformet.2017.10.031","article-title":"Ammonia emissions from a beef feedlot: Comparison of inverse modeling techniques using long-path and point measurements of fenceline NH3","volume":"258","author":"Shonkwiler","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Genedy, R., and Ogejo, J. (2022, January 17\u201320). Estimating ammonia emitted from manure during storage on a dairy farm. Proceedings of the 2022 ASABE Annual International Meeting, Houston, TX, USA.","DOI":"10.13031\/aim.202200410"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115401","DOI":"10.1016\/j.envres.2023.115401","article-title":"Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings","volume":"222","author":"Tagliaferri","year":"2023","journal-title":"Environ. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosenv.2017.09.033","article-title":"Improved quantification of CO2 emission at Campi Flegrei by combined Lagrangian Stochastic and Eulerian dispersion modelling","volume":"170","author":"Pedone","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s10661-018-6488-7","article-title":"Application of open-path Fourier transform infrared spectroscopy for atmospheric monitoring of a CO2 back-production experiment at the Ketzin pilot site (Germany)","volume":"190","author":"Sauer","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_55","unstructured":"Crenna, B.P. (2025, May 20). WindTrax Introductory Manual. Available online: http:\/\/thunderbeachscientific.com\/downloads\/introduction.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.3168\/jds.2019-16864","article-title":"Invited review: Compost-bedded pack barns for dairy cows","volume":"103","author":"Leso","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"El Mashad, H.M., Barzee, T.J., Franco, R.B., Zhang, R., Kaffka, S., and Mitloehner, F. (2023). Anaerobic Digestion and Alternative Manure Management Technologies for Methane Emissions Mitigation on Californian Dairies. Atmosphere, 14.","DOI":"10.3390\/atmos14010120"},{"key":"ref_58","unstructured":"Wolf, K. (2017). A Portable Sensor for Measuring Gas Emissions from Dairy Compost Bedded Pack Barns. [Master\u2019s Thesis, University of Kentucky]."},{"key":"ref_59","first-page":"783","article-title":"Ammonia and Greenhouse Gas Concentrations at Surfaces of Simulated Beef Cattle Bedded Manure Packs","volume":"58","author":"Ayadi","year":"2015","journal-title":"Trans. ASABE"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fuertes, E., Balcells, J., Maynegre, J., de la Fuente, G., Sarri, L., and Seradj, A.R. (2023). Measurement of Methane and Ammonia Emissions from Compost-Bedded Pack Systems in Dairy Barns: Tilling Effect and Seasonal Variations. Animals, 13.","DOI":"10.3390\/ani13111871"},{"key":"ref_61","first-page":"110","article-title":"Spatial variability of methane and carbon dioxide gases in a Compost-Bedded Pack Barn system","volume":"22","author":"Ferreira","year":"2024","journal-title":"Agron. Res."},{"key":"ref_62","unstructured":"3D Flow srl (2025, May 20). 3DF Zephyr, Available online: https:\/\/www.3dflow.net\/it\/software-di-fotogrammetria-3df-zephyr\/."},{"key":"ref_63","unstructured":"QGIS Association (2025, May 20). QGIS Geographic Information System, Available online: https:\/\/www.qgis.org\/."},{"key":"ref_64","first-page":"1","article-title":"Assessment of CO2 emission rate from extended area sources with WindTrax model in a dairy cattle farm","volume":"23","author":"Mattia","year":"2025","journal-title":"Agron. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Becciolini, V., Conti, L., Bedin Marin, D., Merlini, M., Coletti, G., Rossi, U., and Barbari, M. (2022, January 19\u201322). Real-Time Measurements of Gaseous and Particulate Emissions from Livestock Buildings and Manure Stores with Novel UAV-Based System. Proceedings of the AIIA 2022: Biosystems Engineering Towards the Green Deal, Palermo, Italy.","DOI":"10.1007\/978-3-031-30329-6_108"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Becciolini, V., Merlini, M., Massera, E., Bedin Marin, D., Rossi, G., and Barbari, M. (2024, January 17\u201319). Performance of a Multi-sensor System for Ground and Aerial Sampling of Pollutants in Livestock Farms. Proceedings of the AIIA 2024\u2014Mid-Term Conference, Padova, Italy.","DOI":"10.1007\/978-3-031-84212-2_114"},{"key":"ref_67","unstructured":"Crenna, B.P. (2025, May 20). Atmospheric Data in WindTrax. Available online: http:\/\/www.thunderbeachscientific.com\/downloads\/atmosphericdata.pdf."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"7373","DOI":"10.1016\/j.atmosenv.2008.06.019","article-title":"Influence of source\u2013sensor geometry on multi-source emission rate estimates","volume":"42","author":"Crenna","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_69","unstructured":"Mattia, A., Becciolini, V., Rossi, G., Conti, L., Merlini, M., Ferraz, P.F.P., Ferraz, G.A.S., Ferreira, J.C., and de Oliveira, F.M. Proposal of a methodology for quantifying carbon dioxide emissions by customized multi-sensor platforms in a dairy cattle farm: Tilling effect of a compost bedded pack barn. Atmos. Pollut. Res., 2025. submitted."},{"key":"ref_70","unstructured":"R Core Team (2024). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_71","unstructured":"RStudio Team (2024). RStudio: Integrated Development for R, RStudio, Inc.. Available online: http:\/\/www.rstudio.com\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.envsoft.2011.09.008","article-title":"openair\u2014An R package for air quality data analysis","volume":"27\u201328","author":"Carslaw","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_73","unstructured":"Carslaw, D.C. (2019). The Openair Manual\u2014Open-Source Tools for Analysing Air Pollution Data, University of York. Available online: https:\/\/openair-project.github.io\/book\/."},{"key":"ref_74","unstructured":"Hijmans, R. (2025, May 20). raster: Geographic Data Analysis and Modeling, R Package, Available online: https:\/\/rspatial.org\/raster\/."},{"key":"ref_75","unstructured":"Wei, T., and Simko, V. (2025, May 20). R Package \u2018corrplot\u2019: Visualization of a Correlation Matrix. Available online: https:\/\/github.com\/taiyun\/corrplot."},{"key":"ref_76","unstructured":"Wickham, H. (2025, May 20). ggplot2: Elegant Graphics for Data Analysis. Available online: https:\/\/ggplot2.tidyverse.org\/."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Elpelt-Wessel, I., Reiser, M., Morrison, D., and Kranert, M. (2022). Emission Determination by Three Remote Sensing Methods in Two Release Trials. Atmosphere, 13.","DOI":"10.3390\/atmos13010053"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"00021","DOI":"10.1525\/elementa.2022.00021","article-title":"A cautionary report of calculating methane emissions using low-cost fence-line sensors","volume":"10","author":"Riddick","year":"2022","journal-title":"Elem. Sci. Anthr."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1080\/10962247.2020.1811800","article-title":"Methane flux from flowback operations at a shale gas site","volume":"7","author":"Shaw","year":"2020","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"00045","DOI":"10.1525\/elementa.2022.00045","article-title":"Estimating methane emissions from underground natural gas pipelines using an atmospheric dispersion-based method","volume":"10","author":"Tian","year":"2022","journal-title":"Elem. Sci. Anthr."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Billesbach, D., Arkebauer, T., and Sullivan, R. (2024). Intercomparison of sensible and latent heat flux measurements from combined eddy covariance, energy balance, and Bowen ratio methods above a grassland prairie. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-67911-z"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6733\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T05:11:55Z","timestamp":1762319515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,3]]},"references-count":81,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25216733"],"URL":"https:\/\/doi.org\/10.3390\/s25216733","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,3]]}}}