There is widespread reporting of improvements in ambient air quality as a result of the COVID-19 pandemic, but much of the evidence has focused on direct comparisons between air quality measurements made before and after social and travel restrictions were in place.
Such comparisons can often be misleading as the social and travel restrictions are not the only factors affecting measured concentrations of air pollutants.
Ambient concentrations can change significantly over short periods of time simply because of changes to the weather. Statistical models can be used to identify the influence of key meteorological and temporal variables on ambient measurements which allow the effects of meteorology and temporal factors to be nominally removed. It is then possible to identify changes in concentrations which would, in theory, have been recorded if meteorological conditions had remained constant.
This webinar will explore examples of analyses into the effects of COVID-19 restrictions on air quality. It will explain that a failure to take account of variability in underlying factors can easily confound and mislead such analyses. Identification of these confounding influences can thus help to reveal the direct effects of the COVID-19 restrictions.