Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy

Abstract

Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO₂ tropospheric levels using satellite data available at the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO₂ levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia-Romagna. We found little association of NO₂ levels with SARS-CoV-2 prevalence up to about 130 μmol/m², while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.

Keywords: Air pollution; Coronavirus; Covid-19; Nitrogen dioxide; Public health; Sentinel-5P.

Graphical abstract

Fig. 1

Northern Italy study area showing levels of NO₂ tropospheric levels (μmol/m²) before the first lockdown (A), and in the subsequent periods February 24–March 8 (B), March 8–March 22 (C), and after March 22 (D).

Fig. 2

Restricted cubic spline regression analysis between NO₂ tropospheric levels (μmol/m²) before the spread of the outbreak and SARS-CoV-2 positivity prevalence (cases per 100,000) in the three periods after the lockdown dates (A: February 24–March 8; B: March 8–March22; C: March 22–April 5). Results presented SARS-CoV-2 infection prevalence rate (solid line) with 95% confidence interval (dash lines) in a multivariable model adjusted for population density, an index indicating age of the population, people mobility measured from telephone movements before the lockdown, temperature (°C) and relative humidity in the three subsequent periods, and airport presence. Shaded circles are weighted on number of cases corresponding to the prevalence rates at each time point.