  Physics-informed neural networks (NN) are an emerging technique to improve
spatial resolution and enforce physical consistency of data from physics models
or satellite observations. A super-resolution (SR) technique is explored to
reconstruct high-resolution images ($4\times$) from lower resolution images in
an advection-diffusion model of atmospheric pollution plumes. SR performance is
generally increased when the advection-diffusion equation constrains the NN in
addition to conventional pixel-based constraints. The ability of SR techniques
to also reconstruct missing data is investigated by randomly removing image
pixels from the simulations and allowing the system to learn the content of
missing data. Improvements in S/N of $11\%$ are demonstrated when physics
equations are included in SR with $40\%$ pixel loss. Physics-informed NNs
accurately reconstruct corrupted images and generate better results compared to
the standard SR approaches.
