Modelling and Forecasting Temporal PM 2.5 Concentration Using Ensemble Machine Learning Methods

Obuks Augustine Ejohwomu, Olakekan Shamsideen Oshodi, Majeed Oladokun, Oyegoye Teslim Bukoye, Nwabueze Emekwuru, Adegboyega Sotunbo, Olumide Adenuga

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    23 Citations (Scopus)
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    Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.
    Original languageEnglish
    Article number46
    Issue number1
    Early online date4 Jan 2022
    Publication statusPublished - 4 Jan 2022

    Bibliographical note

    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited


    • ensemble machine learning methods
    • modelling and forecasting
    • PM2.5
    • predictive performance


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