Visualization Platform for Multi-Scale Air Pollution Monitoring and Forecast

Air pollution is of great concern for governmental agencies, business sectors and the public due to its adverse impact on human wellness. Effective monitoring and fore-casting of air pollution are essential for stakeholders to take necessary actions for risk management. This paper presents the development of a user-friendly visualization platform that can incorporate deep learning estimation schemes in ensemble with observations and modeling data for air pollution forecast globally, as well as real-time monitoring of those airborne pollutants at a local scale. A pipeline is developed to directly transport forecast information from the extended model and cloud-based real-time ambient data from low-cost sensors. The resulting dashboard offers the possibility to customize various options such as the regions of interest, time scopes of forecast, air pollutant types, and deep learning models used. Interactive and engaging features designed for the platform promote exploring the rendered data from a diversity of air quality models and sources. This integrated solution aims to provide stakeholders with a holistic yet explicit perspective on the spatial-temporal disperses of air pollutants with accuracy and reliability. Such insights into the ambient conditions can contribute to raising environmental awareness, providing input to microclimating, enhancing the ability to make informed decisions, analyzing meteorological patterns, supporting policy formulation, and facilitating proactive climate responsiveness.