Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec;13(12):101620.
doi: 10.1016/j.apr.2022.101620. Epub 2022 Dec 2.

Lessons learnt for air pollution mitigation policies from the COVID-19 pandemic: The Italian perspective

Massimo D'Isidoro  1 , Ilaria D'Elia  1 , Lina Vitali  1 , Gino Briganti  1 , Andrea Cappelletti  1 , Antonio Piersanti  1 , Sandro Finardi  2 , Giuseppe Calori  2 , Nicola Pepe  2 , Alessandro Di Giosa  3 , Andrea Bolignano  3 , Gabriele Zanini  1
Affiliations

Lessons learnt for air pollution mitigation policies from the COVID-19 pandemic: The Italian perspective

Massimo D'Isidoro et al. Atmos Pollut Res. 2022 Dec.

Abstract

Policies to improve air quality need to be based on effective plans for reducing anthropogenic emissions. In 2020, the outbreak of COVID-19 pandemic resulted in significant reductions of anthropogenic pollutant emissions, offering an unexpected opportunity to observe their consequences on ambient concentrations. Taking the national lockdown occurred in Italy between March and May 2020 as a case study, this work tries to infer if and what lessons may be learnt concerning the impact of emission reduction policies on air quality. Variations of NO2, O3, PM10 and PM2.5 concentrations were calculated from numerical model simulations obtained with business as usual and lockdown specific emissions. Both simulations were performed at national level with a horizontal resolution of 4 km, and at local level on the capital city Rome at 1 km resolution. Simulated concentrations showed a good agreement with in-situ observations, confirming the modelling systems capability to reproduce the effects of emission reductions on ambient concentration variations, which differ according to the individual air pollutant. We found a general reduction of pollutant concentrations except for ozone, that experienced an increase in Rome and in the other urban areas, and a decrease elsewhere. The obtained results suggest that acting on precursor emissions, even with sharp reductions like those experienced during the lockdown, may lead to significant, albeit complex, reduction patterns for secondary pollutant concentrations. Therefore, to be more effective, reduction measures should be carefully selected, involving more sectors than those related to mobility, such as residential and agriculture, and integrated on different scales.

Keywords: Air pollution; Air quality modelling; Air quality policies; COVID-19; Lockdown.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Domains considered in the present study: Italian national domain with a resolution of 4 km (on the left, green border) and domains of the Lazio regional system with the inner Rome domain having a resolution of 1 km (on the right, red border, the black star indicating the city location).
Fig. 2
Fig. 2
Top: contribution of each sector (affected by the restrictions) to total BASE emissions over the entire period (Feb–May 2020). Middle: total emission variations (LOCK-BASE)/BASE [%] due to lockdown measures. Bottom: emission variations over the entire period for each sector.
Fig. 3
Fig. 3
NO2 concentration differences (in μg/m3) between LOCK and BASE in April 2020: Italian domain at 4 km resolution (left) and zoomed over Rome (top, right); 1 km resolution simulation over Rome (bottom, right). Gray lines represent Rome main road network.
Fig. 4
Fig. 4
As in Fig. 3 but referred to O3 concentration differences.
Fig. 5
Fig. 5
As in Fig. 3 but referred to PM10 concentration differences.
Fig. 6
Fig. 6
As in Fig. 3 but referred to PM2.5 concentration differences.
See this image and copyright information in PMC

References

    1. Adani M., Piersanti A., Ciancarella L., D'Isidoro M., Villani M.G., Vitali L. Preliminary tests on the sensitivity of the FORAIR_IT air quality forecasting system to different meteorological drivers. Atmosphere. 2020;11 doi: 10.3390/atmos11060574. - DOI
    1. Amato F., Karanasiou A., Moreno T., Alastuey A., Orza J.A.G., Lumbreras J., Borge R., Boldo E., Linares C., Querol X. Emission factors from road dust resuspension in a Mediterranean freeway. Atmos. Environ. 2012;61:580–587. doi: 10.1016/j.atmosenv.2012.07.065. - DOI
    1. ARIA/ARIANET Emission Manager - processing system for model-ready emission input - user's guide. Milano. 2013;19 R2013. No. R2013.19)
    1. AriaNet srl . 2011. SURFPRO3 User's Guide (SURFace-Atmosphere Interface PROcessor. Version 3)
    1. Barré J., Petetin H., Colette A., Guevara M., Peuch V.-H., Rouil L., Engelen R., Inness A., Flemming J., Pérez García-Pando C., Bowdalo D., Meleux F., Geels C., Christensen J.H., Gauss M., Benedictow A., Tsyro S., Friese E., Struzewska J., Kaminski J.W., Douros J., Timmermans R., Robertson L., Adani M., Jorba O., Joly M., Kouznetsov R. Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models. Atmos. Chem. Phys. 2021;21:7373–7394. doi: 10.5194/acp-21-7373-2021. - DOI

LinkOut - more resources