Integrating Geospatial Tools for Air Pollution Prediction: A Synthetic City Generator Framework for Efficient Modeling and Visualization

Abstract

Air pollution is a significant public health and environmental concern that requires accurate prediction and monitoring. This paper introduces a framework that establishes a city-wide abstraction layer for air pollution prediction. The authors present contemporary advancements in air pollution modeling, including research approaches and technologies. The framework promotes a streamlined learning process and improves efficiency by generating a simulated representation of the Earth’s surface for air pollution forecasting using the Land-Use Regression (LUR) model and facilitating data visualization. The authors aim to establish a platform for exchanging research experiences and replicating findings to improve air pollution prediction and control. The framework can help policymakers, researchers, and environmentalists monitor air pollution levels and develop effective strategies to mitigate its adverse effects.