Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

Journal: Environmental research
PMID: 39117059

Abstract

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions.

Authors

  • Julien Vachon
    Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS Du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
  • Jules Kerckhoffs
    Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
  • Stéphane Buteau
    Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS Du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
  • Audrey Smargiassi
    Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS Du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada. Electronic address: audrey.smargiassi@umontreal.ca.

Keywords

External Resources

Popular Topics
Recent Journals