Comments on "Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods"

Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods provides a timely and comprehensive overview of deterministic, statistical, machine learning (ML), and hybrid approaches in air quality modeling. The review effectively summarizes recent developments and highlights emerging trends, such as physics-informed machine learning and integrated forecasting systems. However, several critical operational challenges require further discussion. These include model transferability across data-sparse regions, uncertainty quantification, the interpretability of deep learning architectures, operational robustness, and data quality constraints. Recent advancements in adaptive mesh refinement, AI-assisted chemical transport models, and hybrid deep learning frameworks further underscore the need for explainable, multi-scale, and impact-oriented systems. We argue that future air quality forecasting must move beyond predictive accuracy alone to increasingly integrate atmospheric chemistry, high-resolution observations, rigorous uncertainty analysis, and public health frameworks. Hybrid systems that couple physical interpretability with artificial intelligence represent the most promising frontier for next-generation operational workflows.