A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown. Issue 4 (12th March 2022)

• Record Type: Journal Article
• Authors: Fioravanti, Guido; Cameletti, Michela; Martino, Sara; Cattani, Giorgio; Pisoni, Enrico
• Is Part Of: Environmetrics. Volume 33:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-12
• Subjects: COVID‐19 lockdown -- INLA‐SPDE approach -- intervention analysis/environmental policy -- NO2 relative change -- spatiotemporal statistical model; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2723
• Languages: English
• ISSNs: 1180-4009

Abstract

Abstract: When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify—in space and time—the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS‐CoV‐2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO 2 ) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around − 25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures. There are two aspects of our research that are equally interesting. First, we provide a unique statistical perspective for calculating the relative change in the NO 2 by jointly modeling pollutant concentrations time series. Second, as an output we provide a collection of weekly continuous maps, describing the spatial pattern of the NO 2 2019/2020 relative changes.