2025/01/20 by Osama Ahmad, Zubair Khalid, Ahmad, Osama +5
Earth and Planetary Sciences · Environmental Science · #Air Quality Monitoring and Forecasting #Artificial Intelligence (cs.AI) #Atmospheric chemistry and aerosols #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Impact of Light on Environment and Health #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2501.11270
openalex publication_date 2025/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To address these challenges, this paper proposes a framework for high-resolution spatiotemporal Air Quality Index (AQI) mapping using sparse sensor data, satellite imagery, and various spatiotemporal factors. By leveraging Graph Neural Networks (GNNs), we estimate AQI values at unmonitored locations based on both spatial and temporal dependencies. The framework incorporates a wide range of environmental features, including meteorological data, road networks, points of interest (PoIs), population density, and urban green spaces, which enhance prediction accuracy. We illustrate the use of our approach through a case study in Lahore, Pakistan, where multi-resolution data is used to generate the air quality index map at a fine spatiotemporal scale.