Reply on RC1

Abstract

Worldwide the number of large urban agglomerations is steadily increasing. At a local scale, their emissions lead to air pollution, directly affecting people’s health. On a global scale, their emissions lead to an increase of greenhouse gases, affecting climate. In this context, in 2017 and 2018, the airborne campaigns EMeRGe (Effect of Megacities on the transport and transformation of pollutants on the Regional to Global scales) investigated emissions of European and Asian major population centres (MPCs) to improve the understanding and predictability of pollution outflows. Here, we present two methods to identify and characterise emission outflows probed during EMeRGe. First, we use a set of volatile organic compounds (VOCs) as chemical tracers to characterise air-masses by specific source signals, i.e. benzene from anthropogenic emissions of targeted regions, acetonitrile from biomass burning (BB, primarily during EMeRGe-Asia) and isoprene from fresh biogenic signals (primarily during EMeRGe-Europe). Second, we attribute probed air-masses to source regions and estimate their individual contribution by constructing and applying a simple emission uptake scheme for the boundary layer which combines FLEXTRA back-trajectories and EDGAR carbon monoxide (CO) emission rates (acronyms are provided in Appendix A). During EMeRGe-Europe, we identified anthropogenic emission outflows from Northern Italy, Southern Great Britain, the Belgium-Netherlands-Ruhr area and the Iberian Peninsula. Additionally, our uptake scheme indicates significant long-range transport of emissions from the USA and Canada. During EMeRGe-Asia, the emission outflow is dominated by sources in China and Taiwan, with further emissions (mostly from BB) originating from Southeast Asia and India. Emissions of pre-selected MPC targets are identified in less than 20 % of the sampling time, due to restrictions in flight planning and constraints of the measurement platform itself. Still, EMeRGe combines in a unique way near- and far-field measurements, which show signatures of local and distant sources, transport and conversion fingerprints and complex emission mixtures. Our approach already provides a valuable classification and characterisation of the EMeRGe dataset, e.g. for BB and anthropogenic influence of potential source regions, and paves the way for a more comprehensive analysis and various model studies.