This cluster of papers focuses on the advances in low-cost air quality monitoring systems, utilizing technologies such as neural networks, forecasting models, and IoT-based sensor networks to monitor particulate matter and pollutants in urban environments. The research explores the application of machine learning and environmental monitoring techniques to improve the accuracy and accessibility of air quality data.