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The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94. [ABSTRACT FROM AUTHOR]

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Ventilation rate is equally important as pollutant concentration in aerial emission quantification, but is technically challenging to determine for animal buildings. A simple and reliable method using 240-VAC to 6-VDC unregulated adapters to monitor 24 single-speed wall-mount ventilation fans was applied in a 12-room swine environmental research building. A portable Wall Fan Tester (WFT), consisting of nine equally-distanced impeller anemometers, was designed and developed to measure airflow rates of the wall fans of 508-mm and 356-mm diameters. Fan ventilation rates were tested using the WFT at 5 or 6 static pressures from −1.8 to −133.9 Pa and power voltages from 185 to 245 VAC at 15-VAC increments in five fan test sessions. Fan ventilation models were developed using a single independent variable (static pressure) and two independent variables (static pressure and fan power voltage). The innovative system and methodology considerably improved ventilation rate quantification for the 12 rooms. The study also found that the wall fans in the building delivered 16.9–40.1% less ventilation under different static pressures compared with the fan specifications. There were 17–29% ventilation rate variations among identical fans in different rooms. Daily power supply voltages to the fans often fluctuated for 3–6%, which could introduce errors in ventilation calculations if not taking them into account. Cross-sectional airflow profiles were not always symmetric and there were backflows in some sub-areas for the 356-mm fan inlet at conditions of low power voltages and high negative static pressures. [ABSTRACT FROM AUTHOR]