Dear Colleagues,

Worsening air quality is one of the major global causes of premature mortality and is the main environmental risk, claiming seven million deaths every year. Nearly all urban areas do not comply with the air quality guidelines of the World Health Organization (WHO). This health threat could be diminished by developing models to forecast air quality and inform citizens of the risks of practicing certain activities during elevated pollution episodes.

The traditional predictive approach is based on deterministic models that calculate physical processes and the transport within the atmosphere. The approaches most commonly used by the community are chemical transport models (CTMs) that process the input information of emissions, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with meteorology. However, the reactions between air pollutants and influential factors are highly non-linear, leading to a very complex system of air pollutant formation mechanisms. Therefore, statistical learning (or machine learning) algorithms are increasingly used to account for the proper non-linear modelling of air contamination. Although statistical models do not explicitly simulate the environmental processes, they generally exhibit higher predictive performance than CTMs on fine spatiotemporal scales in the presence of extensive monitoring data.

Several machine learning (ML) approaches have been used in recent years to predict a set of air pollutants using different combinations of predictor parameters. However, with a growing number of studies, why a certain algorithm is chosen over another for a given task is puzzling. The objective of this Special Issue is to gather innovative research studies on ML models of air quality in order to better understand their predictive power. We are especially interested in papers focusing on (i) state-of-the-art algorithms (e.g., support vector machine, ensemble learning, artificial neural networks, extreme learning, deep learning, and hybrid models); (ii) models able to predict pollution peaks; (iii) the prediction of contaminants recently put in the spotlight (e.g., nanoparticles); and (iv) comparative studies between CTM-based and ML-based predictions.

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• air pollution;
• particulate matter, CO_x, NO_x, SO₂, O₃;
• prediction and forecasting;
• statistical modeling;
• data mining and big data;
• support vector machine;
• extreme and deep learning;
• reinforcement learning;
• hybrid models;
• time series analysis