Predicting Fine-Scale Daily NO2 for 2005-2016 Incorporating OMI Satellite Data Across Switzerland

Kees De Hoogh, Apolline Saucy, Alexandra Shtein, Joel Schwartz, Erin A. West, Alexandra Strassmann, Milo Puhan, Martin Roösli, Massimo Stafoggia, Itai Kloog

Research output: Contribution to journalReview articlepeer-review

36 Scopus citations

Abstract

Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- A nd long-term health effects. We aim to model daily average NO2 concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining â58% (R2 range, 0.56-0.64) of the variation in measured NO2 concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained â73% (R2 range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO2 across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO2. The predicted NO2 concentrations will be made available to facilitate health research in Switzerland.

Original languageEnglish
Pages (from-to)10279-10287
Number of pages9
JournalEnvironmental Science & Technology
Volume53
Issue number17
DOIs
StatePublished - 24 May 2019

ASJC Scopus subject areas

  • Chemistry (all)
  • Environmental Chemistry

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