Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. This system was tested on a PC to evaluate cloud prediction and a Raspberry P i to evaluate edge devices’ prediction. It uses the past 24 h of PM 2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM 2.5. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM 2.5 concentration on both edge devices and the cloud. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. Particulate Matter less than 2.5 µm in diameter (PM 2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide.
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