Air Quality Prediction

Air quality prediction aims to forecast pollutant concentrations, primarily focusing on fine particulate matter (PM2.5), to inform public health interventions and environmental policy. Current research emphasizes the development and application of advanced machine learning models, including deep learning architectures like foundation models and physics-guided neural networks, as well as ensemble methods, to improve prediction accuracy and address challenges like data sparsity and spatiotemporal dependencies. These efforts are driven by the significant public health implications of air pollution, with improved forecasting enabling more effective early warning systems, personalized risk mitigation strategies, and data-driven policy decisions. Furthermore, research is exploring the integration of indoor and outdoor air quality prediction, highlighting the interconnectedness of these environments.