Researchers have introduced a novel forecasting framework, leveraging a pre-trained GPT-2 model to predict atmospheric air pollution in areas with limited observational data. This approach, dubbed Meteorology-Driven GPT for Air Pollution, integrates meteorological factors to enhance the accuracy of air pollution forecasts. By utilizing a Gaussian rank-stabilized low-rank approximation, the model achieves parameter efficiency, making it suitable for deployment in data-scarce settings. The framework's multi-task design enables it to simultaneously forecast multiple air pollution-related variables, further improving its overall performance. This development has significant implications for environmental monitoring and policy support, particularly in regions where air pollution data is sparse1. The ability to accurately forecast air pollution levels can inform decision-making and mitigate the adverse effects of poor air quality, making this innovation crucial for practitioners and policymakers working to improve environmental health.