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.
翻译:当采取新的环境政策或具体的干预措施以提高空气质量时,评估并量化(在空间和时间上)已通过的战略的有效性至关重要。2020年全世界为减少SARS-COV-2病毒扩散而采取的封闭措施可以设想为对空气质量产生间接影响的政策干预措施。本文提出一个统计时空模型,作为干预分析的工具,能够考虑到天气和其他复杂因素的影响,以及数据中存在的空间和时间相关性。特别是,我们在此侧重于2019/2020年意大利北部氮氧化物浓度的相对变化(NO$_2美元),在3月和4月期间,该封闭措施生效。我们提供一份每周连续地图汇编,说明2019/2020年NO$2的相对变化。我们发现,在2020年3月和4月期间,所研究地区的多数相对变化是负面的(介于-25%左右),但3月第一周至4月第一周的气候变化是可能持续到4月第一周的气候变化。(从3月第一周到4月第四周的温度变化是可能持续到4月份的一周)。