  Since the outbreak of COVID-19 policy makers have been relying upon
non-pharmacological interventions to control the outbreak. With air pollution
as a potential transmission vector there is need to include it in intervention
strategies. We propose a U-net driven quantile regression model to predict
$PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We
demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on
ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial
distribution, even for locations where pollution data is unavailable. Such
predictions of $PM_{2.5}$ characteristics could crucially advise public policy
strategies geared to reduce the transmission of and lethality of COVID-19.
