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

Abstract

Chemical transport model (CTM) is a fundamental research tool for atmospheric environment and is widely used for air quality forecasting, source apportionment, and strategy design of pollution alleviation (Chuang et al., 2018;Shen et al., 2020;Zhang et al., 2014). Physical transport process is the driving force for primary pollutants, thus then affects the concentration of secondary pollutants (Byun & Schere, 2006;Tilt, 2019). It is usually numerically solved based on the Euler's mass continuity equation in CTM and occupies 57%~60% of the entire CTM's computation time (Colella & Woodward, 1984;Ying & Li, 2011;Zhang et al., 2013). Moreover, the exponential growth of computational cost is observed with finer spatial resolution due to the smaller time step and larger iteration steps required (Boffi et al., 2007;Kasim et al., 2020). The solution of physical transport process has become the critical bottleneck of the CTM's computation efficiency.Machine learning, especially deep learning, has become a new research paradigm and acted as partial or whole replacement of complex geoscience models due to its excellent ability for non-linear fitting (Reichstein et al., 2019;Rolnick et al., 2019;Yuan et al., 2020). Specifically, for the surrogate of atmospheric transport process, Lauret et al. ( 2016) combined cellular automata and the artificial neural network (CA-ANN) to calculate the turbulence coefficient in horizontal two-dimensional (2-D) space. The R squared is over 0.7 for most testcases with the computation efficiency increases by ∼1.5 times. Wang and Qian (2018) extended the study to three-dimensional (3-D) space with the same CA-ANN. R squared for a single time step vary between 0.2 and 0.95, and is reduced to almost zero after 100 multiple-time-step. The computation efficiency increased is still ∼1.5 times. Vlasenko et al. ( 2021) emulated the entire CTM and the speed gain is 720 times of acceleration. Only 2-D horizontal distribution of daily average concentration, however, is concerned and the R squared values are only in the range of 0.38-0.67. The comprehensive performance with the aspects of efficiency promotion, long-term consistency, and spatial dimensions was still unsatisfied.

Emulating CMAQ Simulations with a Conditional U-Net: A Data-Driven Approach for PM2.5 Concentration Prediction

“…Fine particulate matter (PM2.5) pollution has emerged as a significant public health concern, with numerous studies linking PM2.5 exposure to various adverse health outcomes, including respiratory and cardiovascular diseases 12 . In South Korea, PM2.5 concentrations are influenced by both transboundary pollution from China and domestic emissions of PM2.5 precursors.…”