Approximating Three‐Dimensional (3‐D) Transport of Atmospheric Pollutants via Deep Learning

The physical transport process is the bottleneck of the computational efficiency in regional chemical transport modeling. The issue will be worse with the smaller time step due to increased iterations required with finer spatial resolution at scale. Reported surrogates of the transport process are usually unfeasible according to integrated assessment of efficiency promotion, long‐term consistency, and spatial dimensions. This study intended to approximate the three‐dimensional (3‐D) transport process (including advection and diffusion) of a state‐of‐the‐art chemical transport model, that is, Models 3/Community Multiscale Air Quality (CMAQ), via the U‐Net structure of deep learning. Two temporal resolutions of models with 1‐hr and 5‐min were developed. Validation results indicated that single‐step R squared of both models were higher than 0.9, and the lifetime for continuous running was 400 and 1,000 steps for 1‐hr and 5‐min model, respectively. Meanwhile, the computational efficiency can be promoted with the maximum of 164 times for 1‐hr and 14 times for 5‐min resolution on one GPU. The 1‐hr deep learning surrogate could still achieve 12 times acceleration on the same CPU configurations of CMAQ, mainly through the end‐to‐end direct inferring rather than time step iterations. This study preliminarily proves the feasibility of the data‐driven approach in approximating the 3‐D complex transport process of atmospheric pollutants. Furthermore, computational efficiency can be efficiently improved while maintaining consistency and accuracy. Rapid transport simulation of different pollutants with wide concentration range can be expected, which will finally benefit the acceleration of whole chemical transport modeling. Key Points An end‐to‐end deep learning surrogate toward 3‐D atmospheric transport process of pollutants was developed Continuous running of 400 and 1,000 steps were achieved for the 3‐D consistency between the surrogate and the numerical benchmark Maximum of 164 speedup factors for computational efficiency were promoted via the neural network and Graphics Processing Unit architecture