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Predicting Fine-Scale Daily NO 2 for 2005–2016 Incorporating OMI Satellite Data Across Switzerland

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author de Hoogh Kees, Saucy Apolline, Shtein Alexandra, Schwartz Joel, West Erin A., Strassmann Alexandra, Puhan Milo, Röösli Martin, Stafoggia Massimo, Kloog Itai,
Project Investigating the interplay of job exposures, ambient air pollution and living environment as drivers of the chronic lung disease epidemic The Large-scale LuftiBus-SNC Cohort
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Original article (peer-reviewed)

Journal Environmental Science & Technology
Volume (Issue) 53(17)
Page(s) 10279 - 10287
Title of proceedings Environmental Science & Technology
DOI 10.1021/acs.est.9b03107

Open Access

URL https://pubs.acs.org/doi/abs/10.1021/acs.est.9b03107#
Type of Open Access Green OA Embargo (Freely available via Repository after an embargo)

Abstract

Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- and 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 ∼58% (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 ∼73% (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.
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