The potential of drone observations to improve air quality predictions by 4D-Var - H. Erraji; P. Franke; A. Lampert; T. Schuldt; R. Tillmann; A. Wahner; A. C. Lange (Atmospheric Chemistry and Physics)
Vertical profiles of atmospheric pollutants, acquired by uncrewed aerial vehicles (UAVs, known as drones), represent a new type of observation that can help to fill the existing observation gap in the planetary boundary layer (PBL). This article presents the first study of assimilating air pollutant observations from drones to evaluate the impact on local air quality analysis. The study uses the high-resolution air quality model EURAD-IM (EURopean Air pollution Dispersion – Inverse Model), including the four-dimensional variational data assimilation system (4D-Var), to perform the assimilation of ozone (O3) and nitrogen oxide (NO) vertical profiles. 4D-Var is an inverse modelling technique that allows for simultaneous adjustments of initial values and emissions rates. The drone data were collected during the MesSBAR (automated airborne measurement of air pollution levels in the near-earth atmosphere in urban areas) field campaign, which was conducted in Wesseling, Germany, on 22–23 September 2021. The results show that the 4D-Var assimilation of high-resolution drone measurements has a beneficial impact on the representation of regional air pollutants within the model. On both days, a significant improvement in the vertical distribution of O3 and NO is noticed in the analysis compared to the reference simulation without data assimilation. Moreover, the validation of the analysis against independent observations shows an overall improvement in the bias, root mean square error, and correlation for O3, NO, and NO2 (nitrogen dioxide) ground concentrations at the measurement site as well as in the surrounding region. Furthermore, the assimilation allows for the deduction of emission correction factors in the area near the measurement site, which significantly contributes to the improvement in the analysis.