Abstract
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 language | English |
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Article number | 46 |
Journal | Buildings |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - 4 Jan 2022 |
Bibliographical note
Funding Information:This research effort was funded by The University of Manchester?s Global Challenges Research Fund (GCRF) QR grant.
Funding Information:
Acknowledgments: The field study was supported by University of Lagos colleagues and students.
Funding Information:
Funding: This research effort was funded by The University of Manchester’s Global Challenges Research Fund (GCRF) QR grant.
Keywords
- Ensemble machine learning methods
- Modelling and forecasting
- PM
- Predictive performance
ASJC Scopus subject areas
- Architecture
- Civil and Structural Engineering
- Building and Construction