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Near-surface NO2 (NS-NO2) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied to estimate NS-NO2 using satellite-based NO2 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-NO2 with a spatial resolution of 0.1&#x00B0; &#x00D7; 0.1&#x00B0; 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>2 &#x003D; 0.80 and RMSE &#x003D; 9.0&#x00A0;<italic>&#x03BC;</italic>g&#x002F;m3. 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-NO2 is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. Furthermore, the population-weighted NO2-based estimated dataset shows a significant decline of pollution exposure levels from 2014 to 2018.

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO2 of Mainland China

Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO₂ of Mainland China