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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.


Artificial intelligence (AI) household air pollution Internet of Things air pollution prediction

Article Details

Author Biographies

Samson Otieno Ooko, Adventist University of Africa, Kenya

Samson Otieno Ooko is the Registrar at the Adventist University of Africa and is pursuing a PhD in Information Technology. He holds an MSc in IT and an MSc in Internet of Things (IoT). His current interests lie in the integration of machine learning with IoT technologies.

Enatha Rweyemamu, Dares Salaam Institute of Technology, Tanzania

Enatha Rweyemamu is an Instructor II at the Dar es Salaam Institute of Technology in Tanzania, where she serves in the Department of Electronics and Telecommunications Engineering. She holds a Bachelor’s Degree in Electronics and Communication Engineering and a Master of Science in Internet of Things in Embedded Computing Systems. Her research focuses on the application of machine learning in embedded systems.

How to Cite
Ooko, S. O., & Rweyemamu, E. (2024). Monitoring and Predicting African Rural Household Air Pollution Using Internet of Things and Artificial Intelligence. Pan-African Journal of Health and Environmental Science, 3(1), 59–73.


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