The synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution

Abstract

Abstract This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution during the summer of 2017 over the contiguous United States (CONUS). We employed the partial convolutional neural network (PCNN) and the deep neural network (DNN) to fill gaps in the OMI tropospheric NO2 column and estimate the daily proxy surface NO2 map at a spatial resolution of 10 km × 10 km, showing high capability with strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). Subsequently, we conducted an inversion of NOx emissions using the Community Multiscale Air Quality (CMAQ) model at 12 km grid spacing to gain a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion indicated higher NOx emissions over CONUS (3.21 ± 3.34 times), effectively mitigating the underestimation of surface NO2 concentrations with the prior emissions. Incorporating the DL-estimated daily proxy surface NO2 map yielded primary benefits, reducing bias (-1.53 ppb to 0.26 ppb) and enhancing day-to-day variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) across CONUS.