A spatio-temporal analysis of NO$_2$ concentrations during the Italian 2020 COVID-19 lockdown

TL;DR

This study uses a statistical spatio-temporal model to analyze the impact of the 2020 COVID-19 lockdown on NO$_2$ concentrations in northern Italy, revealing significant reductions likely due to the intervention.

Contribution

It introduces a novel spatio-temporal statistical model that accounts for weather and correlation effects to assess environmental policy impacts.

Findings

Most of northern Italy experienced around 25% reduction in NO$_2$ during lockdown

Weather effects do not explain the observed NO$_2$ changes

Spatial maps show significant NO$_2$ decrease during March-April 2020

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 spatio-temporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factors, 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. As an output, we provide a collection of weekly continuous maps, describing the spatial pattern of the NO$_2$ 2019/2020 relative changes. 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.