Article: Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe
Science of the total environment. 2021 Sept. 15, v. 787
2021
Abstract: The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have ...
| Abstract | The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have affected air quality in Europe, we developed Bayesian spatio-temporal (BST) models that assess changes in the surface nitrogen dioxide (NO₂) and fine particulate matter (PM₂.₅) concentration across the continent. We fitted BST models to measurements of the two pollutants in 2020 using a lockdown indicator covariate, while accounting for the spatial and temporal correlation present in the data. Since other factors, such as weather conditions, local combustion sources and/or land surface characteristics may contribute to the variation of pollutant concentrations, we proposed two model formulations that allowed the differentiation between the variations in pollutant concentrations due to seasonality from the variations associated to the lockdown policies. The first model compares the changes in 2020, with the ones during the same period in the previous five years, by introducing an offset term, which controls for the long-term average concentrations of each pollutant during 2014–2019. The second approach models only the 2020 data, but adjusts for confounding factors. The results indicated that the latter can better capture the lockdown effect. The measures taken to tackle the virus in Europe reduced the average surface concentrations of NO₂ and PM₂.₅ by 29.5% (95% Bayesian credible interval: 28.1%, 30.9%) and 25.9% (23.6%, 28.1%), respectively. To our knowledge, this research is the first to account for the spatio-temporal correlation present in the monitoring data during the pandemic and to assess how it affects estimation of the lockdown effect while accounting for confounding. The proposed methodology improves our understanding of the effect of COVID-19 lockdown policies on the air pollution burden across the continent. |
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| Keywords | Bayesian theory ; COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; air pollution ; air quality ; combustion ; environment ; models ; nitrogen dioxide ; pandemic ; particulates ; pollutants ; road transportation ; viruses ; weather ; Europe |
| Language | English |
| Dates of publication | 2021-0915 |
| Publishing place | Elsevier B.V. |
| Document type | Article |
| Note | NAL-AP-2-clean |
| ZDB-ID | 121506-1 |
| ISSN | 1879-1026 ; 0048-9697 |
| ISSN (online) | 1879-1026 |
| ISSN | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2021.147607 |
| Database | NAL-Catalogue (AGRICOLA) |
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