Deep Learning-Based Prediction of Air Pollutant Concentrations Across Seasonal and Environmental Conditions

Abstract

Air pollution has become a major environmental concern because of its harmful effects on human health, ecosystems, and climate. Accurate prediction of pollutant concentrations is therefore essential for effective air-quality management and timely preventive action. This review examines air pollutant concentration prediction using conventional approaches and deep learning-based techniques. It discusses major pollutants, including PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃, together with their spatial and temporal variability and monitoring approaches. The study further explores multi-pollutant prediction, pollutant interactions, and the influence of seasonal and environmental factors such as temperature, humidity, wind speed, rainfall, atmospheric pressure, and solar radiation. Particular emphasis is placed on deep learning-based prediction across varying atmospheric and environmental conditions, including AQI prediction and global air-quality studies. The review also highlights the adverse effects of air pollution on respiratory, cardiovascular, neurological health and ecosystems. Finally, major challenges associated with pollution mitigation, rapid urbanization, technological limitations, infrastructure, public awareness, regulatory barriers, and climate change are discussed. Overall, the study provides a comprehensive perspective on air pollution prediction and supports the development of reliable forecasting strategies for sustainable environmental management and improved public health.