A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO 2 to estimate NO x emissions in China. Combining the information from satellites with surface observations of NO 2 will provide greater constraints on emission estimates of NO x. However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Nitrogen dioxide ( NO 2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides ( NO x = NO + NO 2).
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