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<p><i>Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods</i> 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.</p>

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  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Satish Bhagwatrao Aher,
  • Greeshma C. Ravindran

摘要

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.