Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland

Kees de Hoogh, Harris Héritier, Massimo Stafoggia, Nino Künzli, Itai Kloog

Research output: Contribution to journalArticlepeer-review

95 Scopus citations


Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM2.5) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM2.5 monitoring data was supplemented by imputing PM2.5 concentrations at PM10 sites, using PM2.5/PM10 ratios at co-located sites. Daily PM2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM2.5 in cells with AOD but without PM2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM2.5 concentrations. We present high spatiotemporal models for daily PM2.5 exposure (2003–2013) across Switzerland at 100 × 100 m, using satellite AOD and supplementing sparse monitoring data with the PM10 network.

Original languageEnglish
Pages (from-to)1147-1154
Number of pages8
JournalEnvironmental Pollution
StatePublished - Feb 2018
Externally publishedYes


  • Air pollution
  • Exposure assessment
  • Fine particulate matter
  • Satellite
  • Spatiotemporal models


Dive into the research topics of 'Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland'. Together they form a unique fingerprint.

Cite this