Characterizing the air quality impact of COVID-19 emission reductions through experimental forecasts with the Rapid-Refresh model coupled to chemistry (RAP-Chem)

Abstract:

The COVID-19 global pandemic and associated economic and societal fallout has caused rapid and substantial changes in air pollutant emissions – and subsequently air quality - across the globe. However, current real-time air quality forecasting has been largely unable to capture these changes since the forecasts are driven by emissions developed for a business as usual (BAU) scenario. Even so, the emissions are often several years out of date due to the immense undertaking of updating emission inventories on a more frequent basis. Here we develop new anthropogenic emissions that represent the real-world conditions of the summer of 2020 to produce experimental daily air quality forecasts over North and Central America using the Rapid-Refresh forecast model coupled to chemistry (RAP-Chem). We also simultaneously produce experimental forecasts that use BAU emissions allowing a quantification of the air quality impact of emission reductions due to COVID-19. The simultaneous forecasts will additionally help provide insight into the nonlinear relationship between changes in emissions and air quality across chemical regimes.