An Artificial Intelligence Based Air Pollution Prediction Model for Smart Cities

Abstract

Air pollution is an escalating concern driven by urbanization and population growth, leading to significant health problems. Accurate information on air quality and associated health risks is essential for effective environmental management. In this study, the implementation of Artificial Neural Network (ANN) techniques for hourly, daily, & monthly air pollution prediction is investigated. The study highlights that ANN methods offer superior accuracy in air pollution forecasting compared to traditional approaches. It emphasizes that the accuracy of ANN-based models is influenced by input parameters and the type of architecture algorithms employed. Due to their ability to process a diverse range of meteorological data, ANN models prove to be more reliable and precise than other empirical models. The study also emphasizes the present research gaps pertaining to the utilize of neural networks in air pollution prediction. This analysis aims to stimulate further research and encourage advancements in artificial intelligence for improving air quality forecasting. The proposed system integrates heterogeneous real-time data streams — including ground-level electrochemical sensor readings, satellite-derived atmospheric measurements, vehicular flow indices, and localized meteorological parameters — into a unified analytical pipeline.Spatial interpolation modules further enhance prediction granularity across city zones with uneven sensor coverage. The system delivers pollutant concentration forecasts for particulate matter (PM2.5, PM10), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) at hourly and 48-hour prediction horizons.Beyond technical performance, the framework is engineered for operational integration with Smart City command infrastructure, enabling automated public advisory dissemination, dynamic traffic rerouting triggers, and evidence-based environmental policy support. This study establishes a replicable, data-driven blueprint for intelligent air quality governance in the era of connected urban environments.