A Machine Learning-based Framework for Air Quality Analysis, Ozone Prediction, Procedural Visualization, and Carbon Footprint Assessment

Air Pollution and ozone depletion have continued to be the leading environmental issues since the rise of industrialization and urbanization. The urgency to address this has reached a level where citizens have to replace checking the weather conditions with checking if the air around them is breathable. Although some platforms currently moni-tor Air Pollution or Ozone Depletion in real time, they do not bring together the monitoring, analysis and prediction of correlated environmental topics like Air Pollution, Ozone Depletion and Carbon Footprint. Moreover, the existing systems present these insights with overwhelming data that hinders their easy understanding by the common public. Our system presents a one-stop platform that monitors, analyzes, and predicts the air pollution and ozone depletion around the globe and presents the data in the form of graphs, charts and procedurally generated art. The outcomes are displayed on a responsive 3D Globe that redirects the user to the 2D map of the country, fol-lowed by region-wise dashboards for detailed analysis. Users can also calculate their personalized carbon footprint and analyze the impact of daily actions and make sustainable decisions. It aims to build a platform that can simulate Air Pollution and its ef-fects on Ozone Depletion over years using Procedurally Generated Art by collaborating Environment, Art, Data and Technology.