Field Testing of Low-Cost PM Sensors in Animal Production Facilities

The measurement of particulate matter (PM) in animal housing environments is crucial for ensuring the health and well-being of both animals and human workers. High concentrations of PM can lead to respiratory issues, reduced productivity, and compromised animal welfare. The affordability and compact design of low-cost PM sensors present an opportunity to enhance spatiotemporal resolution in PM measurements. However, these low-cost sensors have certain limitations and require characterization in dusty environments such as animal production facilities. This study examines eight low-cost PM sensors (PMS5003, PMS7003, OPC-R2, OPC-N3, Gravity, SDS011, GP2Y1010, and PPD42) for their performance in monitoring PM1, PM2.5, and PM10 concentrations in animal houses. It details sensor components, hardware integration, and field deployment, along with preliminary testing in farm office and production room environments. A GRIMM 11-D aerosol spectrometer was used as the reference monitor. The OPC-N3 sensor showed high linearity against the reference monitor in the office, with R2 values higher than 0.97, but this correlation dropped to 0.40-0.59 in the production room due to increased particle concentration affecting sensor sensitivity. Meanwhile, the PMS7003 sensor excelled in PM1 measurements with an R² value of 0.90, performing well in production settings, in contrast to its performance in the office. The SDS011 sensor also demonstrated good performance in production environments. Preliminary results suggest that while these sensors effectively measure PM levels under certain conditions, their performance varies significantly depending on environmental factors such as dust concentration, temperature, and relative humidity. The necessity for rigorous field testing and calibration is emphasized to enhance the reliability and accuracy of these sensors in monitoring indoor air quality in agricultural settings. Further research and field testing are essential to validate sensor performance and ensure their effectiveness across diverse environmental conditions.