In-Field Performance Evaluation of an IoT Monitoring System for Fine Particulate Matter in Livestock Buildings

Area of Science:

• Environmental Science
• Agricultural Engineering
• Sensor Technology

Background:

• Livestock farming is a major source of air pollutants, including ammonia and particulate matter (PM2.5).
• Effective monitoring of PM2.5 in barns is essential for developing mitigation strategies.
• Existing monitoring systems can be costly and complex to deploy.

Purpose of the Study:

• To assess the reliability of low-cost sensors for monitoring PM2.5 concentrations in a dairy barn environment.
• To develop and validate a parameter-based adjustment methodology for improving sensor accuracy.
• To evaluate the potential of an IoT system for real-time air quality monitoring in livestock facilities.

Main Methods:

• Validated low-cost PM2.5 sensors against a high-precision reference instrument.
• Collected data from sensors integrated with an IoT platform, including temperature and humidity.
• Developed a parameter-based correction model using statistical assessments.

Main Results:

• Low-cost sensor performance showed a strong correlation with ambient temperature and humidity.
• The proposed correction model significantly improved the accuracy of PM2.5 measurements.
• Corrected sensors demonstrated effectiveness in monitoring mean daily PM2.5 levels in the barn.

Conclusions:

• The validated low-cost sensor system, with the proposed correction model, is a reliable and affordable tool for monitoring PM2.5 in dairy barns.
• The methodology is adaptable for similar sensors and environments, facilitating widespread air quality monitoring.
• Implementing such systems is crucial for effective pollution control in the livestock sector.