A Smart Approach with AI-IoT Synergy to Enhance Educational Spaces: A Real-Time Air Pollution Monitoring System, A Case Study: UR-NYARUGENGE Campus

Abstract

Rapid urbanization and climate change pose serious environmental issues to cities such as Kigali, with considerable air pollution. Public health, safety, and quality of life are all significantly impacted by this problem, especially in educational settings where students and staff are Fragile. Conventional monitoring methods, which are frequently more reactive than proactive, are insufficient to successfully handle these issues. Highlighting the University of Rwanda (UR) at the NYARUGENGE Campus (College of Science and Technology (CST)), this study proposes an Internet of Things (IoT)-enabled air pollution monitoring system specifically made for educational settings. The system seeks to enhance the campus environment by collecting and analyzing air quality data in real-time by using random forest. It consists of setting up an IoT sensor network throughout the NYARUGENGE Campus to track air quality continuously. These sensors send their data to a central system, where time series machine learning algorithms are used to receive, process, and evaluate it. This method makes it possible to spot abnormalities, identify trends, and forecast future circumstances. The incorporation of an SMS warning mechanism that promptly alerts students and campus administrators when air quality standards are surpassed is a crucial component of this system. This guarantees prompt and efficient reactions to possible threats, protecting the campus community's health and well-being. Technology promotes data-driven campus planning, environmental sustainability, and public health outcomes by enabling real-time data collection and predictive analysis. The installation of this IoT- and machine learning using time series algorithm and random forest-based air pollution monitoring system at NYARUGENGE Campus opens the door for more intelligent, secure, and sustainable learning environments by providing a scalable model for other educational institutions dealing with comparable environmental issues.