Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.