  Chemical transport models (CTMs), which simulate air pollution transport,
transformation, and removal, are computationally expensive, largely because of
the computational intensity of the chemical mechanisms: systems of coupled
differential equations representing atmospheric chemistry. Here we investigate
the potential for machine learning to reproduce the behavior of a chemical
mechanism, yet with reduced computational expense. We create a 17-layer
residual multi-target regression neural network to emulate the Carbon Bond
Mechanism Z (CBM-Z) gas-phase chemical mechanism. We train the network to match
CBM-Z predictions of changes in concentrations of 77 chemical species after one
hour, given a range of chemical and meteorological input conditions, which it
is able to do with root-mean-square error (RMSE) of less than 1.97 ppb (median
RMSE = 0.02 ppb), while achieving a 250x computational speedup. An additional
17x speedup (total 4250x speedup) is achieved by running the neural network on
a graphics-processing unit (GPU). The neural network is able to reproduce the
emergent behavior of the chemical system over diurnal cycles using Euler
integration, but additional work is needed to constrain the propagation of
errors as simulation time progresses.
