A Comprehensive Review of Deep Learning Approaches for Air Pollution Forecasting

Abstract

Air pollution has become one of the most critical environmental and public health challenges worldwide, driven by 
rapid urbanization, industrial expansion, transportation growth, and changing climatic conditions. Accurate forecasting of air 
pollutant concentrations is essential for informed policy decisions, emission control strategies, and public health protection. This 
review examines the evolution of air pollution forecasting techniques, beginning with traditional statistical approaches and 
advancing toward modern data-driven deep learning methodologies. Classical models such as ARIMA, Multiple Linear Regression, 
and Kalman Filtering are noted for their interpretability and effectiveness under stable conditions, yet they struggle to represent 
nonlinear atmospheric behavior. Machine learning techniques, including Support Vector Machines, Random Forests, and Artificial 
Neural Networks, improved performance by capturing multivariate dependencies but still lacked the ability to model temporal 
dynamics effectively. Recent advancements in deep learning, particularly hybrid architectures combining convolutional networks 
for feature extraction with recurrent frameworks for sequential learning, have demonstrated superior predictive accuracy by 
capturing complex pollutant–meteorological interactions. However, challenges remain, including limited data quality, high 
computational cost, model interpretability concerns, and difficulties in real-time implementation. This review highlights current 
achievements, identifies methodological gaps, and emphasizes the need for scalable, explainable, and robust forecasting systems 
adaptable to diverse geographic and climatic conditions.