气候变化领域集成服务门户(CCPortal): Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models

CCPortal

DOI	10.5194/acp-21-7373-2021
	Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models
	Barré J.; Petetin H.; Colette A.; Guevara M.; Peuch V.-H.; Rouil L.; Engelen R.; Inness A.; Flemming J.; Pérez García-Pando C.; Bowdalo D.; Meleux F.; Geels C.; Christensen J.H.; Gauss M.; Benedictow A.; Tsyro S.; Friese E.; Struzewska J.; Kaminski J.W.; Douros J.; Timmermans R.; Robertson L.; Adani M.; Jorba O.; Joly M.; Kouznetsov R.
发表日期	2021
ISSN	1680-7316
起始页码	7373
结束页码	7394
卷号	21 期号:9
英文摘要	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. © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
语种	英语
scopus关键词	air quality; atmospheric pollution; COVID-19; nitrogen dioxide; remote sensing; satellite data; satellite imagery; Sentinel
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/246889
作者单位	European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, Reading, United Kingdom; Barcelona Supercomputer Center (BSC), Barcelona, Spain; National Institute for Industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France; Icrea, Catalan Institution for Research and Advanced Studies, Barcelona, Spain; Department of Environmental Science, Aarhus University, Roskilde, Denmark; Norwegian Meteorological Institute, Oslo, Norway; Rhenish Institute for Environmental Research, University of Cologne, Cologne, Germany; Institute of Environmental Protection, National Research Institute, Warsaw, Poland; Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland; Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands; Climate Air and Sustainability Unit, Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Netherlands; Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden; Italian National Agency for New Technologies, Ene...
推荐引用方式 GB/T 7714	Barré J.,Petetin H.,Colette A.,et al. Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models[J],2021,21(9).
APA	Barré J..,Petetin H..,Colette A..,Guevara M..,Peuch V.-H..,...&Kouznetsov R..(2021).Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(9).
MLA	Barré J.,et al."Estimating lockdown-induced European NO2changes using satellite and surface observations and air quality models".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.9(2021).