Air Quality Prediction Based on Machine Learning Algorithms II

Dear Colleagues,

Worsening air quality is one of the major global causes of premature mortality, and is a major environmental risk, claiming seven million deaths every year. Nearly all urban areas fail to 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 most commonly used approaches are chemical transport models (CTMs) that process the input information of emissions, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with meteorological data. However, the reactions between air pollutants and influential factors are highly nonlinear, 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 nonlinear 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.

Prof. Dr. Yves Rybarczyk
Prof. Dr. Rasa Zalakeviciute
Guest Editors

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