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Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (similar to 2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O-3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from-24.9% in Milan to-59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O-3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O-3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOC slimited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of -13-17% counterbalanced by increases up to -9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 degrees C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health. (C) 2020 Elsevier Ltd. All rights reserved. https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://schema.org/encodingFormat application/ld+json https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://schema.org/hasPart https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://schema.org/identifier https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8 https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://schema.org/name Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://w3id.org/ro-id/rohub/model#creation_mode MANUAL https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roevo#LiveRO https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Dataset https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#BibliographyResearchObject https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#BibliographyResearchObject https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Foglini, Federica. "Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8. https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/author mailto:service-account-generation-service https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/contentSize 217 https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/contentUrl https://api.rohub.org/api/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f/download/ https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/creator mailto:service-account-generation-service https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/dateCreated 2021-12-10 09:58:35.222002+00:00 https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/dateModified 2021-12-10 09:58:35.222847+00:00 https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f http://schema.org/description Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (similar to 2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O-3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from-24.9% in Milan to-59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O-3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O-3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOC slimited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of -13-17% counterbalanced by increases up to -9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 degrees C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health. (C) 2020 Elsevier Ltd. 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