Optimizing Aerosol Emissions over Europe using Surface Black Carbon Measurements

One approach to reduce the uncertaintites in the black carbon (BC) emissions estimated using the  bottom-up inventories is by integrating the atmospheric models with observational data. In this study, we estimate aerosol emissions  over Europe (15◦W–35◦E, 33–73 ◦N) by assimilating surface observations of BC from EBAS network using Local Ensemble Transform Kalman Filter (LETKF) in the LOTOS-EUROS chemical transport model. Sensitivity experiments indicate that an ensemble size of 24 and a localization distance of 300 km provide optimal performance. Furthermore, we assess the influence of CAMS BC boundary conditions on the emission estimates and find that these boundary conditions tend to overestimate BC concentrations near the domain boundaries.

Our results show that the bottom-up approach generally overestimates BC emissions across Europe. Quantitatively, the posterior emissions are found to be 21% and 30% lower than the prior emissions for the years 2011 and 2021, respectively. A reduction in both emissions and associated uncertainties is observed over central Europe, where the observations are dense. Seasonal analysis reveals that emission decreases are most pronounced over the central domain during autumn and winter. Finally, the validation of optimized BC concentrations with independent observations showed a decrease in bias and RMSE, however the correlation remains poor compared to the background concentrations.