Modelling and Forecast of Air Pollution Concentrations during COVID Pandemic Emergency with ARIMA Techniques: the Case Study of Two Italian Cities

An efficient and punctual monitoring of air pollutants is very useful to evaluate and prevent possible threats to human beings' health. Especially in areas where such pollutants are highly concentrated, an accurate collection of data could suggest mitigation actions to be implemented. Moreover, a well-performed data collection could also permit the forecast of future scenarios, in relation to the seasonality of the phenomenon. With a particular focus on COVID pandemic period, several literature works demonstrated a decreasing of pollutant concentrations in air of urban areas, mainly for NOx, while CO and PM10, on the opposite, has been observed to remain still, mainly because of the intensive usage of heating systems by the people forced to stay home (on specific regions). With the present contribution the authors here present an application of Time Series analysis (TSA) approach to pollutants concentration data of two Italian cities during first lockdown (9 march – 18 may 2020), demonstrating the possibility to predict pollutants concentration over time. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

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

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. © 2022 Published under licence by IOP Publishing Ltd.