In:
Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 21, No. 9 ( 2021-05-17), p. 7373-7394
Abstract:
Abstract. This study provides a comprehensive assessment of
NO2 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 NO2 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 NO2
column changes with weather-normalized surface NO2 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 NO2 reductions. Locations where the lockdown
measures were stricter show stronger reductions, and, conversely, locations
where softer measures were implemented show milder reductions in NO2
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.
Type of Medium:
Online Resource
ISSN:
1680-7324
DOI:
10.5194/acp-21-7373-2021
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2021
detail.hit.zdb_id:
2092549-9
detail.hit.zdb_id:
2069847-1