Remote Sensing, Vol. 11, Pages 137: A Retrieval of Glyoxal from OMI over China: Investigation of the Effects of Tropospheric NO2 (Remote Sensing)

East China is the `hotspot` of glyoxal (CHOCHO), especially over the Pearl River Delta (PRD) region, where glyoxal is yielded from the oxidation of aromatics. To better understand the glyoxal spatial-temporal characteristics over China and evaluate the effectiveness of atmospheric prevention efforts on the reduction of volatile organic compound (VOC) emissions, we present an algorithm for glyoxal retrieval using the Ozone Monitoring instrument (OMI) over China. The algorithm is based on the differential optical absorption spectroscopy (DOAS) and accounts for the interference of the tropospheric nitrogen dioxide (NO2) spatial-temporal distribution on glyoxal retrieval. We conduct a sensitively test based on a synthetic spectrum to optimize the fitting parameters set. It shows that the fitting interval of 430-458 nm and a 4th order polynomial are optimal for glyoxal retrieval when using the daily mean value of the earthshine spectrum in the Pacific region as a reference. In addition, tropospheric NO2 pre-fitted during glyoxal retrieval is first proposed and tested, which shows a ±10% variation compared with the reference scene. The interference of NO2 on glyoxal was further investigated based on the OMI observations, and the spatial distribution showed that changes in the NO2 concentration can affect the glyoxal result depending on the NO2 spatial distribution. A method to prefix NO2 during glyoxal retrieval is proposed in this study and is referred to as OMI-CAS. We perform an intercomparison of the glyoxal from the OMI-CAS with the seasonal datasets provided by different institutions for North China (NC), South China (SC), the Yangtze River Delta (YRD) and the ChuanYu (CY) region in southwestern China in the year 2005. The results show that our algorithm can obtain the glyoxal spatial and temporal variations in different regions over China. OMI-CAS has the best correlations with other datasets in summer, with the correlations between OMI-CAS and OMI-Harvard, OMI-CAS and OMI-IUP, and OMI-CAS and Sciamachy-IUP being 0.63, 0.67 and 0.67, respectively. Autumn results followed, with the correlations of 0.58, 0.36 and 0.48, respectively, over China. However, the correlations are less or even negative for spring and winter. From the regional perspective, SC has the best correlation compared with other regions, with R reaching 0.80 for OMI-CAS and OMI-IUP in summer. The discrepancies between different glyoxal datasets can be attributed to the fitting parameters and larger glyoxal retrieval uncertainties. Finally, useful recommendations are given based on the results comparison according to region and season.