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- W4383896342 abstract "Abstract This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO x 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 NO 2 column and estimate the daily proxy surface NO 2 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 NO x 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 NO x emissions over CONUS (3.21 ± 3.34 times), effectively mitigating the underestimation of surface NO 2 concentrations with the prior emissions. Incorporating the DL-estimated daily proxy surface NO 2 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." @default.
- W4383896342 created "2023-07-12" @default.
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- W4383896342 date "2023-07-11" @default.
- W4383896342 modified "2023-10-16" @default.
- W4383896342 title "The synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution" @default.
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- W4383896342 doi "https://doi.org/10.21203/rs.3.rs-3129355/v1" @default.
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