Satellite NO2 observations and model simulations of tropospheric columns over South-eastern Europe
1Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
2Royal Netherlands Meteorological Service, De Bilt, The Netherlands
3Belgian Institute for Space Aeronomy, Brussels, Belgium
4Institute of Environmental Physics, University of Bremen, Germany
Abstract. Satellite observations of nitrogen dioxide (NO2) tropospheric columns over Southeastern Europe are analyzed to study the characteristics of the spatial and temporal variability of pollution in the area. The interannual variability of the tropospheric NO2 columns is presented over urban, rural and industrial locations based on measurements from four satellite instruments, GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura and GOME-2/MetOp spanning a period of over twelve years. The consistency between the different datasets over the area is investigated. Two operational algorithms for the retrieval of tropospheric NO2 are considered, the one developed jointly by the Royal Netherlands Meteorological Institute and Belgian Institute for Space Astronomy and the one developed by the University of Bremen. The tropospheric NO2 columns for the area under study have been simulated for the period 1996–2001 with the Comprehensive Air Quality Model (CAMx) and are compared with GOME measurements. Over urban and industrial locations the mean tropospheric NO2 columns range between 3 and 7.0×1015 molecules/cm2, showing a seasonal variability with a peak to peak amplitude of about 6.0×1015 molecules/cm2, while the background values over rural sites are close to 1.1×1015 molecules/cm2. Differences in the overpass time and spatial resolution of the different satellites, as well as differences in the algorithms, introduce significant differences in the estimated columns however the correlation between the different estimates is higher than 0.8. It is found that the model simulations reveal similar spatial patterns as the GOME observations, a result which is consistent with both algorithms. Although the model simulations show a mean bias of −0.1 under clean conditions, the modeled temporal correlation of 0.5 is poor in absence of biogenic and biomass burning emissions.