Air Sentinel: An IoT-Based Platform for Monitoring Indoor Air Quality in Elementary Schools of the Global South

Indoor air quality (IAQ) is neglected in schools of the Global South. Carbon dioxide (CO ₂ ) and particulate matter (PM _2.5 ) are indicators of ventilation and exposure risk to airborne infections, respectively. We deployed Air Sentinel , a decentralized mobile-driven IoT network, to monitor these indicators across 106 spaces in 53 elementary schools in the San Luis Potosí Metropolitan Area, Mexico. Machine learning was used to forecast CO ₂ and PM _2.5 concentrations one hour in advance, and the Wells-Riley model, based on CO ₂ concentration, to estimate airborne infection probability (AIP). IAQ was poor, acceptable, and excellent in 27.3%, 36.4%, and 67.2% of the classrooms, respectively. During the cold season, 97.67% of the classroom CO ₂ levels were typical (400–1000 ppm); in the hot season, 67.67% of the classroom CO ₂ levels were typical, and 1.19% exceeded the high exposure threshold (> 2000 ppm). Classroom CO ₂ dynamics exhibited low temporal synchrony. The strongest forecasting performance for CO ₂ occurred in the hot season, but the PM _2.5 forecast failed in either season while AIP increased during the first two hours of class in both seasons. The successful CO ₂ forecasting model has potential for real-time IAQ management in the cold season. The failure to forecast PM _2.5 levels suggests that localized sources drive their dynamics. We conclude that the Air Sentinel network is a convenient classroom IAQ monitor in the Global South.