Air quality in Germany as a contributing factor to morbidity from COVID-19

Abstract

Background: The SARS-CoV-2 virus has been spreading in Germany since January 2020, with regional differences in incidence, morbidity, and mortality. Long-term exposure to air pollutants as nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), and particulate matter (<10 μm PM₁₀, <2.5 μm PM_2.5) has a negative impact on respiratory functions. We analyze the association between long-term air pollution and the outcome of SARS-CoV-2 infections in Germany.

Methods: We conducted an observational study in Germany on county-level, investigating the association between long-term (2010-2019) air pollutant exposure (European Environment Agency, AirBase data set) and COVID-19 incidence, morbidity, and mortality rate during the first outbreak of SARS-CoV-2 (open source data Robert Koch Institute). We used negative binominal models, including adjustment for risk factors (age, sex, days since first COVID-19 case, population density, socio-economic and health parameters).

Results: After adjustment for risk factors in the tri-pollutant model (NO₂, O₃, PM_2.5) an increase of 1 μg/m³ NO₂ was associated with an increase of the need for intensive care due to COVID-19 by 4.2% (95% CI 1.011-1.074), and mechanical ventilation by 4.6% (95% CI 1.010-1.084). A tendency towards an association of NO₂ with COVID-19 incidence was indicated, as the results were just outside of the defined statistical significance (+1.6% (95% CI 1.000-1.032)). Long-term annual mean NO₂ level ranged from 4.6 μg/m³ to 32 μg/m³.

Conclusions: Our results indicate that long-term NO₂ exposure may have increased susceptibility for COVID-19 morbidity in Germany. The results demonstrate the need to reduce ambient air pollution to improve public health.

Keywords: Air pollution; Intensive care medicine; Mechanical ventilation; Mortality; Nitrogen dioxide; SARS-CoV-2.

Fig. 1

Title: Air pollution metrics for 2010 through 2019, Legend: The plots show air pollution metrics that are averaged across all 10 years by county on a log scale.

Fig. 2

Title: COVID-19 parameters in numbers and per 100,000 residents by county, Legend: COVID-19 parameters in numbers and per 100,000 residents by county evaluated in the models for the dates given above on a log scale. For counties that were reporting but had e.g., no deaths, a zero is shown. The counties that are white had no hospitals reporting DIVI data and were therefore left out of the analysis.