A research team led by Professor Hongliang Zhang from Fudan University, in collaboration with the University of Manchester, has published a comprehensive review demonstrating how deep learning is reshaping atmospheric science and air pollution forecasting. The study, available at https://doi.org/10.1007/s11783-025-2092-6, outlines how artificial intelligence offers an adaptive, data-driven pathway to decode atmospheric complexity beyond traditional physics-based models. Air pollution continues to pose a severe global health threat, claiming millions of lives each year. Traditional chemical transport and climate-chemistry models face limitations due to massive computational requirements and often outdated emission inventories, restricting rapid, high-resolution forecasts needed for early warning systems.
Deep learning addresses these constraints by capturing complex patterns through multi-sensor data assimilation, integrating satellite imagery, ground monitoring, and meteorological observations to fill data gaps caused by cloud interference or sparse monitoring networks. The review highlights how deep learning generates seamless, high-resolution pollution maps by fusing massive, heterogeneous data sources and uncovering patterns invisible to traditional models. However, current models still struggle during extreme pollution events when accurate forecasts matter most. Researchers identify transfer learning, ensemble prediction, and synthetic event generation as promising methods to boost model resilience during critical pollution episodes.
A crucial advancement discussed in the study involves physics-informed neural networks, which embed chemical and physical laws into AI architectures to bridge scientific understanding with computational prediction. This approach moves beyond black-box models toward interpretable, physically grounded forecasting frameworks. The authors also advocate for probabilistic and Bayesian approaches to quantify uncertainty, enabling forecasts that not only predict pollution events but also indicate confidence levels in those predictions. Professor Hongliang Zhang emphasized the importance of making air quality forecasting both smarter and more trustworthy.
By blending physics-based reasoning with deep learning capabilities, researchers aim to open the black box of AI and make its decisions explainable to policymakers and the public. This integration allows stakeholders to understand why pollution events may occur and how to prevent them, transforming prediction into prevention and data into actionable decisions. Deep learning is positioned to become a cornerstone of intelligent environmental governance, with the ability to deliver real-time, data-driven forecasts that empower governments to issue faster warnings, plan emission reductions, and protect vulnerable populations. The fusion of AI with climate-chemistry models also enables seasonal and long-term predictions critical for anticipating climate change effects on air quality. This approach represents a fundamental shift from reactive pollution measures to proactive management, potentially leading to cleaner skies, healthier cities, and a more sustainable planet.
