Abstract

Air pollution poses a growing threat to public health in rapidly urbanizing cities, particularly in Sub-Saharan Africa, where real-time monitoring infrastructure is limited. This paper presents the design and implementation of a scalable IoT and cloud-based architecture for air quality monitoring and intelligent alerting in Accra, Ghana. The system integrates low-cost ESP32-based sensor nodes with a Deep Q-Network (DQN) to classify pollution severity and issue adaptive, context-aware alerts. Eight key environmental parameters, including PM1.0, PM2.5, PM10, VOCs, CO, LPG, temperature, and humidity, are continuously monitored and analyzed using cloud-based processing. Real-time data is visualized through a web dashboard, while critical alerts are disseminated via SMS to ensure user accessibility. The DQN agent supports decision transparency through Q-values, feature importance, and temporal trend analysis. Experimental results demonstrate a training accuracy of 89% and a field test classification accuracy of 82.9%, confirming the system’s effectiveness for scalable, real-time, and interpretable environmental health monitoring in resource-constrained settings.

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