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When a new environmental policy or a specific intervention is taken in orderto improve air quality, it is paramount to assess and quantify - in space andtime - the effectiveness of the adopted strategy. The lockdown measures takenworldwide in 2020 to reduce the spread of the SARS-CoV- 2 virus can beenvisioned as a policy intervention with an indirect effect on air quality. Inthis paper we propose a statistical spatio-temporal model as a tool forintervention analysis, able to take into account the effect of weather andother confounding factors, as well as the spatial and temporal correlationexisting in the data. In particular, we focus here on the 2019/2020 relativechange in nitrogen dioxide (NO$_2$) concentrations in the north of Italy, forthe 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 thespatial pattern of the NO$_2$ 2019/2020 relative changes. We found that duringMarch and April 2020 most of the studied area is characterized by negativerelative changes (median values around -25%), with the exception of the firstweek of March and the fourth week of April (median values around 5%). As thesechanges cannot be attributed to a weather effect, it is likely that they are abyproduct of the lockdown measures.

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