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• W3184676433 endingPage "128425" @default.
• W3184676433 startingPage "128425" @default.
• W3184676433 abstract "The COVID-19 pandemic, as a worldwide threat to public health, has led many governments to impose mobility restrictions and adopt partial or full lockdown strategies in many regions to control the disease outbreak. Although these lockdowns are imposed to save public health by reducing the transmission of the virus, rather significant improvements of the air quality in this period have been reported in different areas, mainly as a result of the reduction in vehicular trips. In this research, the city of Turin in the northern part of Italy has been considered as the study area, because of its special meteorology and geographic location in one of the most polluted regions in Europe, and also its high density of vehicular emissions. A Lagrangian approach is applied to illustrate and analyze the effect of imposing full lockdown restrictions on the reduction of traffic-induced air pollution in the city. To do this, the real-time traffic flow during the lockdown period is recorded, and by utilizing CALPUFF version 7, the dispersion of PM2.5, Total Suspended Particulate (TSP), Benzo(a)pyrene (BaP), NOx, and Black Carbon (BC) emitted from all circulating vehicles during and before the lockdown period are compared. Results indicate that the concentration of pollutants generated by road traffic sources (including passenger cars, busses, heavy-duty vehicles, light-duty vehicles, mopeds, and motorcycles) reduced at least 70% (for PM2.5) up to 88.1% (for BaP) during the studied period. Concentration maps show that the concentration reduction varied in different areas of the town, mainly due to the characteristics and strength of the emission sources and the geophysical features of the area." @default.
• W3184676433 created "2021-08-02" @default.
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• W3184676433 date "2021-10-01" @default.
• W3184676433 modified "2023-10-18" @default.
• W3184676433 title "Traffic-induced atmospheric pollution during the COVID-19 lockdown: Dispersion modeling based on traffic flow monitoring in Turin, Italy" @default.
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• W3184676433 doi "https://doi.org/10.1016/j.jclepro.2021.128425" @default.
• W3184676433 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8297952" @default.
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• W3184676433 hasPublicationYear "2021" @default.
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