IoT-Based Monitoring System of Ammonia Emissions in Naturally Ventilated Buffalo Barns: Sensor Network Development

Ammonia emissions from livestock farming pose an environmental challenge, contributing to air pollution, ecosystem acidification and eutrophication. As one of the primary sources of NH3 emissions, the livestock sector must take an active role in addressing this issue. To enhance air quality across the EU, the European Directive 2016/2284/EU (NEC Directive - National Emission Ceilings) sets specific national reduction targets for pollutant emissions, requiring each member state to implement effective mitigation strategies. Accurate monitoring is the first step toward complying with these regulations, assess the environmental impact and implement targeted mitigation measures. However, measuring NH3 emissions from naturally ventilated barns, such as those of the Italian Mediterranean Buffalo, poses significant challenges due to variable climatic conditions. This study aims to develop an IoT-based monitoring network to measure NH3 and CO2 concentrations in buffalo barns in Campania. CO2 sensors will help estimate barn ventilation rates. Electrochemical DOL 53 sensors to detect ammonia and DOL 119 sensors to measure CO2 were employ and chosen based on their high accuracy, durability and suitability in demanding agricultural settings. The sensor network relies on LoRaWAN technology for data transmission, ensuring a stable and secure connection over distances of up to 15 km. The devices are housed in certified enclosures designed to withstand dust, humidity, and corrosive agents commonly found in livestock environments. The collected data is sent to a central gateway and then transmitted to a dedicated IoT platform, where it is processed, managed, and displayed in real time. This system enables continuous environmental monitoring without interference, allowing for the assessment of both daily and seasonal variations. The next steps will involve the installation of sensors in the barn supported by an airflow dynamic modeling process to improve sampling quality and increase the understanding of emissions dispersion.