Special Issue "Air Quality Prediction Based on Machine Learning Algorithms"
Deadline for manuscript submissions: 31 December 2019
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 approach 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.
Prof. Dr. Yves Rybarczyk
Prof. Dr. Rasa Zalakeviciute
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Air pollution
- Particulate matter, COx, NOx, 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