Machine learning approaches for outdoor air quality modelling: a systematic review

Yves Rybarczyk, Rasa Zalakeviciute

Research output: Contribution to journalReview articlepeer-review

76 Citations (Scopus)
212 Downloads (Pure)


Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

Original languageEnglish
Article number2570
JournalApplied Sciences (Switzerland)
Issue number12
Publication statusPublished - 11 Dec 2018


  • Atmospheric pollution
  • Data mining
  • Multiple correspondence analysis
  • Predictive models


Dive into the research topics of 'Machine learning approaches for outdoor air quality modelling: a systematic review'. Together they form a unique fingerprint.

Cite this