Rigorous quantification of statistical significance of the COVID-19 lockdown effect on air quality: The case from ground-based measurements in Ontario, Canada

Abstract

Preliminary analyses of satellite measurements from around the world showed drops in nitrogen dioxide (NO₂) coinciding with lockdowns due to the COVID-19 pandemic. Several studies found that these drops correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical protocol that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO₂ (11 out 16 sites) and CO (all 4 sites) in April-December 2020, with pollutant levels 20% lower than in the previous three years. Fewer sites (2-3 out of 16) experienced statistically significant drops in O₃ and PM2.5. The statistical significance testing framework developed here is the first of its kind applied to air quality data. It highlights the benefit of a rigorous assessment of statistical significance, should analyses of pollutant levels post COVID-19 lockdowns be used to inform policy decisions.

Keywords: Air quality; COVID-19 pandemic; Canada; Natural meteorological variability; Southern Ontario; Statistical analysis.