Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO<sub>2</sub> of Mainland China

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 measurement...

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Bibliographic Details
Main Authors: Donghui Li, Kai Qin, Jason Cohen, Qin He, Shuo Wang, Ding Li, Xiran Zhou, Xiaolu Ling, Yong Xue
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9557824/
Description
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&#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><sup>2</sup> &#x003D; 0.80 and RMSE &#x003D; 9.0&#x00A0;<italic>&#x03BC;</italic>g&#x002F;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.
ISSN:2151-1535