Air Quality Prediction with Spatio-Temporal Graph Convolutional Networks (ST-GCNs): Integrating Graph-based Deep Learning Models

Air pollution is an important environmental factor influencing public health, and the ability to predict air quality trends provides actionable insights for policymakers and public health authorities. Spatio-Temporal Graph Convolutional Networks (ST-GCNs) are an advanced approach to predict air quality by combining the power of graph neural networks (GNNs) with temporal modeling. These models take into account both the spatial relationships (between sensors or monitoring stations) and temporal dependencies (how pollution levels evolve over time) to make more accurate predictions for air quality forecasting. This paper investigates the use of Spatio-Temporal Graph Convolutional Networks (ST-GCNs) for forecasting air quality and its implications for public health metrics. The study focuses on the ability of ST-GCN models to effectively capture both spatial and temporal dependencies within air quality data, providing more accurate predictions of pollutant levels over time. By combining Graph Convolutional Networks (GCNs), which model the spatial relationships between air quality sensors and monitoring stations, with spatio-temporal modeling, the proposed approach allows for a more holistic view of how air quality evolves in complex urban environments.