A Review of Machine Learning Models for Predicting Air Quality in Urban Areas
El El-Sayed
Abstract
Air pollution is a critical environmental issue that threatens almost the world, and public health, ecosystems, and the sustainability of cities are affected by the severe impacts of air pollution. Urbanization and industrialization have been on the run, with escalating pollution levels. Hence, air monitoring and air quality prediction are necessary for such challenges. This review discusses advanced machine learning (ML), deep learning (DL) techniques, and IoT-based study hybrid frameworks for air-quality prediction in urban settings. Integration of different data sets such as meteorological parameters, concentrations of pollutants, and data from satellite imagery, these technologies provide strong and scalable solutions for real-time monitoring and forecasting. Some of the advancements include the use of IoT-enabled sensors, the use of convolutional and recurrent neural networks, and the development of location-specific predictive models. Despite significant evolution, several challenges of data sparsity, computational requirements, and model adaptability remain. This paper casts the technologies as transforming cities into smart and green cities and advancing the cause for continuous innovation and interdisciplinary collaboration to strengthen their effectiveness. These findings add to the advancement of knowledge on air quality prediction methodologies and their crucial role in sustainable urban development.