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


de Hoogh, Kees; Saucy, Apolline; Shtein, Alexandra; Schwartz, Joel; West, Erin A; Strassmann, Alexandra; Puhan, Milo; Röösli, Martin; Stafoggia, Massimo; Kloog, Itai (2019). Predicting Fine-Scale Daily NO for 2005-2016 Incorporating OMI Satellite Data Across Switzerland. Environmental Science & Technology, 53(17):10279-10287.

Abstract

Nitrogen dioxide (NO) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO 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% ( range, 0.56-0.64) of the variation in measured NO concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% ( 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 NO 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 NO. The predicted NO concentrations will be made available to facilitate health research in Switzerland.

Abstract

Nitrogen dioxide (NO) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO 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% ( range, 0.56-0.64) of the variation in measured NO concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% ( 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 NO 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 NO. The predicted NO concentrations will be made available to facilitate health research in Switzerland.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:3 September 2019
Deposited On:16 Oct 2019 13:26
Last Modified:16 Oct 2019 13:28
Publisher:American Chemical Society (ACS)
ISSN:0013-936X
OA Status:Closed
Publisher DOI:https://doi.org/10.1021/acs.est.9b03107
PubMed ID:31415154

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