  Air pollution is a major driver of climate change. Anthropogenic emissions
from the burning of fossil fuels for transportation and power generation emit
large amounts of problematic air pollutants, including Greenhouse Gases (GHGs).
Despite the importance of limiting GHG emissions to mitigate climate change,
detailed information about the spatial and temporal distribution of GHG and
other air pollutants is difficult to obtain. Existing models for surface-level
air pollution rely on extensive land-use datasets which are often locally
restricted and temporally static. This work proposes a deep learning approach
for the prediction of ambient air pollution that only relies on remote sensing
data that is globally available and frequently updated. Combining optical
satellite imagery with satellite-based atmospheric column density air pollution
measurements enables the scaling of air pollution estimates (in this case
NO$_2$) to high spatial resolution (up to $\sim$10m) at arbitrary locations and
adds a temporal component to these estimates. The proposed model performs with
high accuracy when evaluated against air quality measurements from ground
stations (mean absolute error $<$6$~\mu g/m^3$). Our results enable the
identification and temporal monitoring of major sources of air pollution and
GHGs.
