Since the COVID-19 pandemic began, space and ground-based observations have shown how Earth's atmosphere has observed significant reductions in some air pollutants. Many studies, all over the world, demonstrated how the governmental restrictions imposed because of the spreading of the virus had positive and negative effects on the environment. In this paper, authors discuss how the levels of concentrations of some pollutants varied, in two case studies in Italy, because of the imposed lockdown during the coronavirus pandemic. The extent of the variations CO and PM10 has been evaluated by comparing data registered by local monitoring stations, related to the baseline February-May, of three different years, 2018, 2019 and 2020. In order to better assess the variation of the temporal trend of pollutants before (2018, 2019) and during COVID-19 lockdown (2020) proper physic-mathematical models have been applied to the datasets. The calibration and validation of AutoRegressive Integrated Moving Average (ARIMA) models on interesting series of CO and PM10 data complete the work.

An Application of ARIMA modelling to air pollution concentrations during covid pandemic in Italy

Mancini S.
;
Francavilla A.;Graziuso G.;Guarnaccia C.
2022

Abstract

Since the COVID-19 pandemic began, space and ground-based observations have shown how Earth's atmosphere has observed significant reductions in some air pollutants. Many studies, all over the world, demonstrated how the governmental restrictions imposed because of the spreading of the virus had positive and negative effects on the environment. In this paper, authors discuss how the levels of concentrations of some pollutants varied, in two case studies in Italy, because of the imposed lockdown during the coronavirus pandemic. The extent of the variations CO and PM10 has been evaluated by comparing data registered by local monitoring stations, related to the baseline February-May, of three different years, 2018, 2019 and 2020. In order to better assess the variation of the temporal trend of pollutants before (2018, 2019) and during COVID-19 lockdown (2020) proper physic-mathematical models have been applied to the datasets. The calibration and validation of AutoRegressive Integrated Moving Average (ARIMA) models on interesting series of CO and PM10 data complete the work.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4778950
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