  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.
