Harnessing deep learning for air pollution forecasting: trends, techniques, and future prospects

Abstract

Air pollution is a serious global public health threat arising from exposure to toxic ambient pollutants, including particulate matter (PM), sulphur oxides (SOx), nitrogen oxides (NOx), ozone (O₃), carbon monoxide (CO), and ammonia (NH₃). Traditional statistical and deterministic forecasting models often fail to adequately represent nonlinear interactions among multiple pollutants, meteorological drivers, and anthropogenic influences, motivating the growing adoption of deep learning (DL) approaches. This systematic review synthesizes evidence from more than 150 peer-reviewed studies conducted across diverse geographical regions and employing a wide range of DL architectures, including standalone, hybrid, and advanced spatiotemporal models. Using structured quantitative summaries, rank-based performance comparisons, and methodological assessments, the review identifies leading model families, analyzes pollutant- and horizon-specific performance trends, and evaluates robustness and generalizability across spatial and temporal contexts. Overall, DL models generally outperform traditional approaches, particularly when multi-source inputs and spatiotemporal dependencies are explicitly modeled. Nevertheless, the literature remains fragmented, with a strong concentration of studies in data-rich urban regions of Asia, heterogeneous datasets, inconsistent evaluation protocols, limited transparency, and weak external validity. Addressing these limitations requires standardized preprocessing and benchmarking practices, improved explainability and uncertainty quantification, and the development of globally representative datasets. Emerging directions, including hybrid, physics-informed, and generative DL architectures, offer promising pathways to enhance reliability and operational deployment. Collectively, this review provides a comprehensive and critical synthesis of DL-based air quality forecasting, offering actionable insights for researchers, practitioners, and policymakers seeking transparent, generalizable, and policy-relevant prediction systems for environmental management and public health protection.