Scholarly record
AIR QUALITY TRENDS IN LIVESTOCK BARNS
Abstract
Livestock barns are integral to modern agriculture, providing essential environments for raising animals. The air quality in these barns directly impacts livestock health, quality of production and environmental sustainability. Trends in key metrics such as gas concentrations, particulate matter levels, humidity content, and temperature can influence the overall health of livestock, barn operations, and broader ecological factors. Understanding these trends and their interrelationships is crucial for managing barn conditions effectively. To investigate air quality trends in livestock barns, data was collected using IoT multiple sensors device. These sensors recorded various metrics, including gas concentrations (hydrogen sulfide, ammonia, volatile organic compounds), particulate matter levels (PM2.5 and PM10), and humidity-related metrics such as relative humidity and dewpoint temperature. The collected data was analyzed using correlation and trend analyses to explore relationships between these variables and their influence on barn conditions. The analysis reveals key trends and positive correlations between the collected data.
Publication Impact Profile
Publication details
References9
Chmielowiec-Korzeniowska A., Trawinska B., Tymczyna L., Bis-Wencel H., Matuszewski L. Microbial contamination of the air in livestock buildings as a threat to human and animal health�a review. Annals of Animal Science21(2):417�431, 2021. DOI: 10.2478/aoas-2020-0080
Dineva K., Atanasova T. Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud. Animals, MDPI, vol. 13(20), 3254, 2023. DOI: 10.3390/ani13203254
Benic M. Behaviour and well-being of dairy cows in modern livestock production. Veterinarska stanica, 55(2), 2024. DOI: 10.46419/vs.55.2.3
Mostafa E., Pa?mann J., Abdellatif H. R. S., Buescher W. Improving the forecast of fine dust emission and transmission from cattle barns: a comprehensive data package and analysis. Environmental Sciences Europe, 36(42), 2024. DOI: 10.1186/s12302-024-00845-5
Dineva K., Atanasova T. Machine Learning Solution for IoT Big Data. 20th International Multidisciplinary Scientific GeoConference SGEM, 20(2.1), 2020. DOI: 10.5593/sgem2020/2.1/s07.027
Hewitt B. C., Smit L.A.M., van Kersen W., Wouters I.M., Heederik D.J.J., Kerckhoffs J., Hoek G., de Rooij M.M.T. Residential exposure to microbial emissions from livestock farms: Implementation and evaluation of land use regression and random forest spatial models. Environmental Pollution. April, 1;346:123590. 2024. DOI: 10.1016/j.envpol.2024.123590
Tabase R. K., N?ss G., Larring Yn. Ammonia and methane emissions from small herd cattle buildings in a cold climate. Science of The Total Environment, p. 166046, 2023. DOI: 10.1016/j.scitotenv.2023.166046
Burns A. M., Chandler G., Dunham K. J., Carlton A. G. Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operation. Environ. Sci. Technol. 57(49), 20718�20725, 2023. DOI: 10.1021/acs.est.3c06947
Leliveld L. M. C. Brandolese C., Grotto M., Marinucci A., Fossati N., Lovarelli D., Riva E., Provolo G. Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial far Computers and Electronics in Agriculture, 216, article id: 108499, 2024. DOI: 10.1016/j.compag.2023.108499
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.

