Zeeman: A Deep Learning Framework for Regional Atmospheric Chemistry Forecasting

Abstract Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric components—such as and ozone—the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces a DL‐based framework–Zeeman for atmospheric chemistry forecasting. Our model effectively captures the nuanced relationships among these constituents and while achieving a 68.5‐fold increase in computational speed compared to traditional numerical model. Evaluations demonstrate that our approach rivals numerical model, offering an efficient solution for atmospheric chemistry forecasting. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.