SO2 Validation of GOME-2, OMI and SCIAMACHY by Ground-based MAX-DOAS Measurements in the Beijing Area
Wang, Ting1; Van Roozendael, Michel1; Wang, Pucai2; Hendrick, François1; Fayt, Caroline1; Yu, Huan1; Gielen, Clio1; Pinardi, Gaia1; Hermans, Christian1

Sulfur dioxide (SO2) is a short-lived gas, produced primarily by volcanoes, power plants, refineries, metal smelting and burning of fossil and biofuel. Here we focus on the anthropogenic SO2 emitted by pollution sources in the area of Beijing (40° N, 116° E) and Xianghe (40° N, 117° E) in North East China.

Satellite sensors provide tropospheric SO2 column measurements with global coverage and high spatiotemporal resolution. Concurrent measurements from different satellite sensors, GOME-2 aboard MetOp-A, SCIAMACHY aboard ENVISAT and OMI aboard Aura, at different overpass times, 9:30 local time for GOME-2, 10:00 for SCIAMACHY and 13:30 for OMI, offer the opportunity to observe the diurnal variation of the SO2 columns in highly polluted regions such as Northern China.

In this work, we make use of a state-of-the-art Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument, which has been designed and jointly operated by BIRA-IASB and IAP in the Beijing city center from July 2008 to April 2009, and in Xianghe from March 2010 until present. SO2 data sets retrieved in these two sites are employed for the validation of the satellite sensors.

The ground-based MAX-DOAS measurements are first analysed to derive the diurnal variation of SO2 columns and vertical distributions in both Xianghe and Beijng. To this end, an optimal estimation retrieval scheme developed at BIRA-IASB is being used, which also provides information on the aerosol load in the lowermost troposphere. The variability of the SO2 content measured by the MAX-DOAS instrument is first characterized in relation with the local meteorology and a-priori knowledge on the location of main sources. Then ground-based measurements are applied for the validation of satellite data from the GOME-2, SCIAMACHY and OMI sensors. Various sensitivity analysis are performed to investigate the main sources of errors on both ground-based and satellite data sets, including uncertainties on satellite air mass factors.