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dc.contributor.authorPetetin, Hervé
dc.contributor.authorBowdalo, Dene
dc.contributor.authorSoret, Albert
dc.date.accessioned2020-10-29T16:14:40Z
dc.date.available2020-10-29T16:14:40Z
dc.date.issued2020-05
dc.identifier.citationPetetin, H.; Bowdalo, D.; Soret, A. Assessment of the impact of the Covid-19 lockdown on air pollution over Spain using machine learning. A: . Barcelona Supercomputing Center, 2020, p. 18-20.
dc.identifier.urihttp://hdl.handle.net/2117/330993
dc.description.abstractThe rapid spread of the Covid-19 pandemic over Spain recently forced the Spanish authorities to implement drastic measures of social distancing through a strict lockdown of the population starting on March 14th. As hospitalizations were still strongly increasing, a second and more stringent phase of the lockdown was implemented from the March 30th to April 9th with workers from all non-essential economical activities forced to stay at home. This situation has impacted numerous activity sectors, including road transport, air traffic and part of the industries. As a consequence, air pollutant emissions have been greatly reduced. Although such a large change of emission forcing is expected to reduce the air pollutant concentrations in Spanish urban areas, the extent of such reductions remains uncertain. Key to this uncertainty are the highly variable meteorological conditions that can either attenuate or amplify changes of air pollution concentration originally driven by changes of emissions. Thus, assessing the impact of the Covid-19 lockdown solely based on the analysis of the concentration time series can often be misleading since at least part of the variability is driven by the meteorology. In this study, we explore the use of machine learning algorithms for estimating the business-as-usual NO2 concentrations that would have been observed without the Covid-19 lockdown based on ERA5 meteorological data and additional time features. Trained on past data, these ML models can learn the complex relationships between meteorology and NO2 concentrations, indirectly assuming an average emission forcing. By using these ML models to predict the NO2 concentrations under the current situation (with very different emission forcing), we expect the discrepancies between predictions and observations to be related to a large extent to the reduction of emissions induced by the lockdown regardless of the meteorological conditions. In this study, the reduction of NO2 pollution is investigated in all 50 provinces of Spain.
dc.format.extent3 p.
dc.languageen
dc.language.isoeng
dc.publisherBarcelona Supercomputing Center
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshHigh performance computing
dc.subject.lcshPollution -- Measurement
dc.subject.lcshCOVID-19 Pandemic, 2020- (Spain)
dc.subject.lcshMachine learning
dc.subject.lcshAir quality
dc.subject.otherair quality
dc.subject.othermachine learning
dc.subject.othercovid-19
dc.titleAssessment of the impact of the Covid-19 lockdown on air pollution over Spain using machine learning
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.subject.lemacContaminació -- Mesurament
dc.subject.lemacPandèmia de COVID-19, 2020- (Espanya)
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAire -- Qualitat
dc.rights.accessOpen Access
local.citation.startingPage18
local.citation.endingPage20


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