  Accelerating the numerical integration of partial differential equations by
learned surrogate model is a promising area of inquiry in the field of air
pollution modeling. Most previous efforts in this field have been made on
learned chemical operators though machine-learned fluid dynamics has been a
more blooming area in machine learning community. Here we show the first trial
on accelerating advection operator in the domain of air quality model using a
realistic wind velocity dataset. We designed a convolutional neural
network-based solver giving coefficients to integrate the advection equation.
We generated a training dataset using a 2nd order Van Leer type scheme with the
10-day east-west components of wind data on 39$^{\circ}$N within North America.
The trained model with coarse-graining showed good accuracy overall, but
instability occurred in a few cases. Our approach achieved up to 12.5$\times$
acceleration. The learned schemes also showed fair results in generalization
tests.
