Intelligent Air Quality Monitoring and Management System for Smart Cities

Abstract

Urban air pollution has emerged as one of the foremost environmental and public health crises confronting rapidly growing cities worldwide. The convergence of industrial expansion, vehicular traffic, and urban sprawl continues to degrade ambient air quality at an alarming pace. Conventional monitoring setups, which rely on fixed sensor nodes and dedicated stations, incur substantial deployment costs and inherently limit spatial coverage. This paper introduces an Intelligent Air Quality Monitoring and Management System (IAQMMS) tailored for smart city ecosystems—a software-centric framework that fuses real-time pollutant data streams, machine learning inference, and predictive analytics to monitor, forecast, and govern urban air pollution. The system exploits publicly accessible air quality application programming interfaces (APIs) and historical pollution repositories to generate forward-looking Air Quality Index (AQI) estimates and pinpoint probable emission hotspots. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture is adopted to simultaneously capture spatial pollutant distributions and temporal concentration dynamics. Supplementary capabilities encompass automated health risk stratification and context-aware precautionary guidance delivered through an interactive dashboard. Experimental results confirm competitive prediction accuracy, real-time responsiveness, and seamless scalability, affirming the system's suitability for next-generation smart city deployments.