The Impact of the Lockdown Restrictions on Air Quality During COVID-19 Pandemic in Lombardy, Italy

Paolo Maranzano () and Alessandro Fassó ()
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Paolo Maranzano: Management and Statistics (DEMS), University of Milano-Bicocca, Dept. of Economics
Alessandro Fassó: Dept. of Economics (DSE), University of Bergamo

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 343-374 from Springer

Abstract: Abstract Environmental agencies and scientists around Europe have reported that COVID-19 lockdown caused an extended environmental clean-up. Considering air quality, we focus on the Lombardy region (Northern Italy), which is at the same time the most populous region and the area most affected by COVID-19 in Italy. Lombardy is also one of the most polluted areas in the European Union. The central research hypothesis concerns if and how the first-wave restrictions imposed during the 2020 spring have improved the air quality in Lombardy and if the improvements are similar throughout the territory. To answer these questions, we use weekly data from January 2015 to mid-June 2020 for 74 ground monitoring stations and provided by the regional environmental protection agency (ARPA Lombardia). We estimate an autoregressive time series model with exogenous covariates (ARX) to assess the combined impact of meteorology, seasonality, trend, and lockdown on the NO2 concentrations at each monitoring site. We also propose using the LASSO algorithm to select the set of relevant covariates to model the concentrations and then estimate the effect of lockdown restrictions with a maximum likelihood post-LASSO estimator. Statistical modelling confirms a generalised NO2 reduction due to the lockdown throughout the whole region, despite considerable variability due to the morphological and geographical heterogeneity of Lombardy. Compared to the observed average variations, the estimated lockdown impacts are mitigated by meteorology and natural trends. Expectantly, the most significant and remarkable NO2 reductions have been estimated near urban and congested areas and in the proximity of industrialised sites.

Keywords: COVID-19 lockdown; Lombardy; Air quality; NO2; ARX models; LASSO algorithm (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_15

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DOI: 10.1007/978-3-031-07155-3_15

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