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Abstract:In this study, we report on airborne Differential Optical Absorption Spectroscopy (DOAS) observations of tropospheric NO 2 using an Ultralight Trike (ULT) and associated flux calculations. The instrument onboard the ULT was developed for measuring the tropospheric NO 2 Vertical Column Density (VCD) and it was operated for several days between 2011 and 2014, in the South-East of Romania. Collocated measurements were performed using a car-DOAS instrument. Most of the airborne and mobile ground-based measurements were performed close to an industrial platform located nearby Galati city (45.43 • N, 28.03 • E). We found a correlation of R = 0.71 between tropospheric NO 2 VCDs deduced from airborne DOAS observations and mobile ground-based DOAS observations. We also present a comparison between stratospheric NO 2 Slant Column Density (SCD) derived from the Dutch OMI NO 2 (DOMINO) satellite data product and stratospheric SCDs obtained from ground and airborne measurements. The airborne DOAS observations performed on 13 August 2014 were used to quantify the NO 2 flux originating from an industrial platform located nearby Galati city. Measurements during a flight above the industrial plume showed a maximum tropospheric NO 2 VCD of (1.41 ± 0.27) × 10 16 molecules/cm 2 and an associated NO 2 flux of (3.45 ± 0.89) × 10 −3 kg/s.

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R 2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.

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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.