Summary: | Near-surface NO<sub>2</sub> (NS-NO<sub>2</sub>) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO<sub>2</sub> using satellite-based NO<sub>2</sub> column measurements, there still exist significant defects, resulting in a low overall fit and significant amount of bias. This article combines GOME-2B and OMI satellite data to estimate daily NS-NO<sub>2</sub> with a spatial resolution of 0.1° × 0.1° from 2014 to 2018 over Mainland China, using a machine learning method. The estimated result has four important characteristics. First, the sample-based cross validation with surface observations shows a good result with <italic>R</italic><sup>2</sup> = 0.80 and RMSE = 9.0 <italic>μ</italic>g/m<sup>3</sup>. Second, the underestimation in high concentration areas and overestimation in low concentration areas are both reduced, compared with the case of using OMI data alone. Third, the estimated NS-NO<sub>2</sub> is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. Furthermore, the population-weighted NO<sub>2</sub>-based estimated dataset shows a significant decline of pollution exposure levels from 2014 to 2018.
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