Fassò Alessandro, Maranzano Paolo, Otto Philipp
University of Bergamo, DSE, Via Caniana 2, Bergamo, 24127, BG, Italy.
Leibniz University Hannover, Appelstrasse 9a, Hannover, 30167, Lower Saxony, Germany.
Spat Stat. 2022 Jun;49:100549. doi: 10.1016/j.spasta.2021.100549. Epub 2021 Oct 29.
During the first wave of the COVID-19 pandemics in 2020, lockdown policies reduced human mobility in many countries globally. This significantly reduces car traffic-related emissions. In this paper, we consider the impact of the Italian restrictions (lockdown) on the air quality in the Lombardy Region. In particular, we consider public data on concentrations of particulate matters (PM and PM) and nitrogen dioxide, pre/during/after lockdown. To reduce the effect of confounders, we use detailed regression function based on meteorological, land and calendar information. Spatial and temporal correlations are handled using a multivariate spatiotemporal model in the class of hidden dynamic geostatistical models (HDGM). Due to the large size of the design matrix, variable selection is made using a hybrid approach coupling the well known LASSO algorithm with the cross-validation performance of HDGM. The impact of COVID-19 lockdown is heterogeneous in the region. Indeed, there is high statistical evidence of nitrogen dioxide concentration reductions in metropolitan areas and near trafficked roads where also PM concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.
在2020年新冠疫情的第一波期间，封锁政策降低了全球许多国家的人员流动。这显著减少了与汽车交通相关的排放。在本文中，我们考虑了意大利的限制措施（封锁）对伦巴第地区空气质量的影响。具体而言，我们考虑了封锁前/期间/之后颗粒物（PM和PM）和二氧化氮浓度的公开数据。为了减少混杂因素的影响，我们使用基于气象、土地和日历信息的详细回归函数。使用隐藏动态地理统计模型（HDGM）类中的多元时空模型来处理空间和时间相关性。由于设计矩阵规模较大，使用一种将著名的LASSO算法与HDGM的交叉验证性能相结合的混合方法进行变量选择。新冠疫情封锁的影响在该地区是异质的。确实，在大城市地区以及交通繁忙道路附近，有很高的统计证据表明二氧化氮浓度降低，同时PM浓度也降低。然而，农村、工业和山区并未显示出显著降低。此外，无论哪个区域，PM浓度都没有显著降低。封锁后的重启显示出不明确的结果。
