Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

Autor: Hugo Denier van der Gon, Jeroen Kuenen, Oriol Jorba, Kim Serradell, Carles Tena, Marc Guevara, Carlos Pérez García-Pando, Hervé Petetin, Dene Bowdalo, Albert Soret, Vincent-Henri Peuch
Přispěvatelé: Barcelona Supercomputing Center
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Atmospheric Chemistry and Physics, Vol 21, Pp 773-797 (2021)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Atmospheric Chemistry and Physics
ISSN: 1680-7324
1680-7316
Popis: We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement. The research leading to these results has received funding from the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. We acknowledge support from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R, the Agencia Estatal de Investigacion (AEI) as part of the VITALISE project (PID2019-108086RA-I00/AEI/0.13039/501100011033), the AXA Research Fund, and the European Research Council (grant no. 773051, FRAGMENT). We also acknowledge PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT-2020-1-0007 projects. This project has also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. Carlos Pérez García-Pando also acknowledges support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the MICINN.
Databáze: OpenAIRE
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