Optimization of sampling intervals for particulate matter concentration monitoring in a large naturally ventilated dairy barn

Generation and emission of particulate matter (PM) from dairy farming have a potential effect on the health and welfare of the animals, farm workers and even the neighbors. Monitoring accuracy of the PM concentration depends much on the number and location of sampling points as well as the sampling interval (SI). Most PM studies used intermittent sampling methods, such as sampling the concentration for a couple of days in a season or in several seasons, which were unable to accurately reflect the actual PM concentration level and variation inside the intensive dairy building. To determine the reasonable SI of PM sensors, this study developed an Internet of Things (IoT)-based monitoring system for PM concentration in an intensive naturally ventilated dairy barn, in which a 17-point continuous concentration monitoring of PM less than 2.5 μm in aerodynamic diameter (PM_2.5) and the total suspended particulate (TSP) was carried out in autumn and winter, and its 5-minute mean values were regarded as relatively true values (RTV). Using error analysis, the daily averaged PM concentration with 30 min and 1, 2, 3, 6, 12 h SI in autumn and winter and the hourly mean PM concentration with 10, 15, 20, 30 min and 1 h SI during the day (05:00-23:00), night (23:00-05:00) and daily management periods (05:00-07:00, 13:00-15:00, 21:00-23:00) were first computed, respectively; then their relative errors ( E_r ) with RTV were counted within 5% and 10% range; and finally, the maximum accepted SI for daily and hourly mean PM concentration measurements were determined based on acceptance criteria in bioanalytical method (66.7%).The results showed that within 5%, when the SI for TSP concentration were set within 2 h (in autumn) and 1h (in winter), and they were within 3 h (in autumn) and 1 h (in winter) for PM_2.5 measuring, respectively. It can accurately obtain the daily average PM concentration of the naturally ventilated dairy barn in autumn and winter. When the SI were at 20 min (in autumn) and 15 min (in winter) in daytime, and 30 min (in autumn) and 15 min (in winter) in nighttime for the TSP measurements, and 30 min for PM_2.5 daytime and nighttime (in autumn and winter), an accurate monitoring could be obtained on hourly mean PM concentration and its fluctuations. When the sampling interval for TSP was 10 min, and the interval for PM_2.5 was on 20 min in autumn and winter, respectively, the measurement data can reflect the impact of daily management on the PM concentration inside the barn. The findings of this study can be applied as a standardized procedure to continuously track the PM concentration in an intensive naturally ventilated dairy barn.