Monitoring and Predicting African Rural Household Air Pollution Using Internet of Things and Artificial Intelligence

Abstract

Abstract According to a 2020 report from the World Health Organization (WHO), household air pollution has led to over 3 million deaths globally, with recent statistics showing a worsening situation in Africa. Integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies can help address this global challenge. IoT enables real-time data collection for monitoring pollution levels, while AI algorithms predict pollution before it reaches hazardous levels. However, existing solutions are not tailored to the African context, where wood fuel is a primary pollutant, and they predominantly focus on monitoring rather than prediction. This study presents the design and implementation of an IoT-based solution for monitoring and predicting indoor air pollution in rural African households. The system collects data in real time and transmits it to the cloud for storage, processing, and analysis, with alerts to users when pollution is detected. An AI model was successfully trained and tested to predict indoor air pollution based on the collected data. The results indicate that this approach significantly improves the accuracy and timeliness of pollution alerts, potentially reducing health risks associated with indoor air pollution. The successful implementation and testing of the system demonstrate its potential for broader applications in various indoor environments.