TY - JOUR

T1 - Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown

AU - Fassò, Alessandro

AU - Maranzano, Paolo

AU - Otto, Philipp

PY - 2022/6

Y1 - 2022/6

N2 - 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 10 and PM 2.5) 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 10 concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM 2.5 concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.

AB - 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 10 and PM 2.5) 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 10 concentration is reduced. However, rural, industrial, and mountain areas do not show significant reductions. Also, PM 2.5 concentrations lack significant reductions irrespective of zone. The post-lockdown restart shows unclear results.

KW - COVID-19

KW - Model selection

KW - Multivariate spatiotemporal models

KW - Nitrogen dioxide

KW - Particulate matters

UR - http://www.scopus.com/inward/record.url?scp=85118585973&partnerID=8YFLogxK

U2 - 10.1016/j.spasta.2021.100549

DO - 10.1016/j.spasta.2021.100549

M3 - Article

VL - 49

JO - Spatial statistics

JF - Spatial statistics

SN - 2211-6753

M1 - 100549

ER -