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% (R
2 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% (R
2 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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