Aarhus University Seal

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Youchen Shen, Utrecht University
  • ,
  • Kees de Hoogh, Swiss Tropical and Public Health Institute, University of Basel
  • ,
  • Oliver Schmitz, Utrecht University
  • ,
  • Nicholas Clinton, Alphabet Inc.
  • ,
  • Karin Tuxen-Bettman, Alphabet Inc.
  • ,
  • Jørgen Brandt
  • Jesper H. Christensen
  • Lise M. Frohn
  • Camilla Geels
  • Derek Karssenberg, Utrecht University
  • ,
  • Roel Vermeulen, Utrecht University
  • ,
  • Gerard Hoek, Utrecht University

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

Original languageEnglish
Article number107485
JournalEnvironment International
Publication statusPublished - Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

    Research areas

  • Geographically and temporally weighted regression, Land-use regression, Random forest, Spatially varying coefficient, Spatiotemporal variation

See relations at Aarhus University Citationformats

ID: 280647181