Air pollution is one of the most serious environmental threats in urban areas, affecting both human health and climate. Traditional air quality monitoring systems provide only point-based information; hence, this limits their ability to show distributions across a city. Herein, this work describes HEAL, a web-based system for pollution hotspot predictions and visualizations through the utilization of machine learning and data visualization techniques. This system collects air quality data from APIs or sensors, processes it, and generates dynamic heat maps that showcase the levels of pollution in real time. Interpreting the interaction among environmental, traffic, and meteorological data, HEAL offers citizens, policymakers, and researchers new localized insights into air quality variations, which will result in better decision-making.
Patil et al. (2026) studied this question.