First observation of tropospheric nitrogen dioxide from the Environmental Trace Gases Monitoring Instrument onboard the GaoFen-5 satellite

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作者
Chengxin Zhang
Cheng Liu
Ka Lok Chan
Qihou Hu
Haoran Liu
Bo Li
Chengzhi Xing
Wei Tan
Haijin Zhou
Fuqi Si
Jianguo Liu
机构
[1] University of Science and Technology of China,School of Earth and Space Sciences
[2] University of Science and Technology of China,Department of Precision Machinery and Precision Instrumentation
[3] Anhui Institute of Optics and Fine Mechanics,Key Laboratory of Environmental Optics and Technology
[4] Chinese Academy of Sciences,Center for Excellence in Regional Atmospheric Environment
[5] Institute of Urban Environment,Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes
[6] Chinese Academy of Sciences,Remote Sensing Technology Institute (IMF)
[7] University of Science and Technology of China,undefined
[8] German Aerospace Center (DLR),undefined
来源
Light: Science & Applications | / 9卷
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摘要
The Environmental Trace Gases Monitoring Instrument (EMI) is the first Chinese satellite-borne UV–Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale. The EMI instrument onboard the GaoFen-5 satellite was launched on 9 May 2018. In this paper, we present the tropospheric nitrogen dioxide (NO2) vertical column density (VCD) retrieval algorithm dedicated to EMI measurement. We report the first successful retrieval of tropospheric NO2 VCD from the EMI instrument. Our retrieval improved the original EMI NO2 prototype algorithm by modifying the settings of the spectral fit and air mass factor calculations to account for the on-orbit instrumental performance changes. The retrieved EMI NO2 VCDs generally show good spatiotemporal agreement with the satellite-borne Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument (correlation coefficient R of ~0.9, bias < 50%). A comparison with ground-based MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) observations also shows good correlation with an R of 0.82. The results indicate that the EMI NO2 retrieval algorithm derives reliable and precise results, and this algorithm can feasibly produce stable operational products that can contribute to global air pollution monitoring.
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