Details
| Original language | English |
|---|---|
| Article number | 100549 |
| Journal | Spatial statistics |
| Volume | 49 |
| Early online date | 29 Oct 2021 |
| Publication status | Published - Jun 2022 |
Abstract
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.
Keywords
- COVID-19, Model selection, Multivariate spatiotemporal models, Nitrogen dioxide, Particulate matters
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Environmental Science(all)
- Management, Monitoring, Policy and Law
Sustainable Development Goals
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In: Spatial statistics, Vol. 49, 100549, 06.2022.
Research output: Contribution to journal › Article › Research › peer review
}
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 -