The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns

Abstract. The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns took place in Romania in September 2014 and August 2015. They focused on two sites: the Bucharest urban area and the power plants in the Jiu Valley. Their main objectives were to test recently developed airborne observation systems dedicated to air quality studies and to verify the concept of such campaigns in support of the validation of spaceborne atmospheric missions such as TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P). We show that tropospheric NO2 vertical column density (VCD) measurements using airborne mapping instruments are valuable for satellite validation. The signal to noise ratio of the airborne NO2 measurements is one order of magnitude higher than its spaceborne counterpart when the airborne measurements are averaged at the TROPOMI pixel scale. A significant source of comparison error appears to be the time variation of the NO2 VCDs during a flight, which we estimated at about 4 x 1015 molec cm-2 in the AROMAT conditions. Considering the random error of the TROPOMI tropospheric NO2 VCD (σ), the dynamic range of the NO2 VCDs field extends from detection limit up to 37σ (2.6 x 1016 molec cm-2) or 29σ (2 x 1016 molec cm-2) for Bucharest and the Jiu Valley, respectively. For the two areas, we simulate validation exercises of the TROPOMI tropospheric NO2 product using airborne measurements. These simulations indicate that we can closely approach the TROPOMI optimal target accuracy of 25 % by adding NO2 and aerosol profile information to the mapping data, which constrains the investigated accuracy within 28 %. In addition to NO2, we also measured significant amounts of SO2 in the Jiu Valley, as well as a hotspot of H2CO in the center of Bucharest. For these two species, we conclude that the best validation strategy would consist in deploying ground-based measurement systems at key locations which the AROMAT observations help identify.