Estimating lockdown-induced European NO 2 changes using satellite and surface observations and air quality models
2021
Abstract. This study provides a comprehensive assessment of
NO 2 changes across the main European urban areas induced by COVID-19
lockdowns using satellite retrievals from the Tropospheric Monitoring
Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site
measurements, and simulations from the Copernicus Atmosphere Monitoring
Service (CAMS) regional ensemble of air quality models. Some recent
TROPOMI-based estimates of changes in atmospheric NO 2 concentrations
have neglected the influence of weather variability between the reference
and lockdown periods. Here we provide weather-normalized estimates based on
a machine learning method (gradient boosting) along with an assessment of
the biases that can be expected from methods that omit the influence of
weather. We also compare the weather-normalized satellite-estimated NO 2
column changes with weather-normalized surface NO 2 concentration
changes and the CAMS regional ensemble, composed of 11 models, using
recently published estimates of emission reductions induced by the lockdown.
All estimates show similar NO 2 reductions. Locations where the lockdown
measures were stricter show stronger reductions, and, conversely, locations
where softer measures were implemented show milder reductions in NO 2
pollution levels. Average reduction estimates based on either satellite
observations ( − 23 %), surface stations ( − 43 %), or models ( − 32 %) are
presented, showing the importance of vertical sampling but also the
horizontal representativeness. Surface station estimates are significantly
changed when sampled to the TROPOMI overpasses ( − 37 %), pointing out the
importance of the variability in time of such estimates. Observation-based
machine learning estimates show a stronger temporal variability than
model-based estimates.
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