Development of a Real-Time IoT-Based Air Quality Monitoring System for Crowded University Lecture Halls

Indoor air quality in university lecture halls has a direct impact on student health, cognitive performance, and learning outcomes, particularly in densely populated academic environments. Studies report that poorly ventilated classrooms can experience carbon dioxide (CO₂) levels exceeding 1,500 ppm, a threshold associated with reduced attention and decision-making performance, while particulate matter (PM₂.₅) concentrations in crowded indoor spaces may be 2–5 times higher than recommended limits during peak occupancy. In many developing regions, continuous air quality monitoring within educational buildings remains limited due to cost and infrastructure constraints. This paper presents the development of a real-time IoT-based air quality monitoring system designed specifically for crowded university lecture halls. The proposed system integrates low-power environmental sensors to measure key indoor air quality parameters, including temperature, humidity, and particulate matter, with real-time data transmission to a cloud-based monitoring platform. The system architecture emphasizes affordability, scalability, and ease of deployment within existing building infrastructure. Experimental deployment in representative lecture halls demonstrates the system’s ability to capture occupancy-driven air quality variations, with observed PM₂.₅ levels increasing by over 40% during peak lecture periods compared to unoccupied conditions. The results validate the necessity of continuous indoor air quality monitoring and highlight the system’s potential to support evidence-based ventilation management, improve indoor environmental quality, and enhance student well-being within smart university campuses. Strategies for artificial intelligence (AI) adoption in sustainable construction projects: