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    Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements
    (Katlenburg-Lindau : EGU, 2019) Marinou, Eleni; Tesche, Matthias; Nenes, Athanasios; Ansmann, Albert; Schrod, Jann; Mamali, Dimitra; Tsekeri, Alexandra; Pikridas, Michael; Baars, Holger; Engelmann, Ronny; Voudouri, Kalliopi-Artemis; Solomos, Stavros; Sciare, Jean; Groß, Silke; Ewald, Florian; Amiridis, Vassilis
    Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot.
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    Megacity and local contributions to regional air pollution: An aircraft case study over London
    (Katlenburg-Lindau : EGU, 2020) Ashworth, Kirsti; Bucci, Silvia; Gallimore, Peter J.; Lee, Junghwa; Nelson, Beth S.; Sanchez-Marroquín, Alberto; Schimpf, Marina B.; Smith, Paul D.; Drysdale, Will S.; Hopkins, Jim R.; Lee, James D.; Pitt, Joe R.; Di Carlo, Piero; Krejci, Radovan; McQuaid, James B.
    In July 2017 three research flights circumnavigating the megacity of London were conducted as a part of the STANCO training school for students and early career researchers organised by EUFAR (European Facility for Airborne Research). Measurements were made from the UK's Facility for Airborne Atmospheric Measurements (FAAM) BAe-146-301 atmospheric research aircraft with the aim to sample, characterise and quantify the impact of megacity outflow pollution on air quality in the surrounding region. Conditions were extremely favourable for airborne measurements, and all three flights were able to observe clear pollution events along the flight path. A small change in wind direction provided sufficiently different air mass origins over the 2 d such that a distinct pollution plume from London, attributable marine emissions and a double-peaked dispersed area of pollution resulting from a combination of local and transported emissions were measured. We were able to analyse the effect of London emissions on air quality in the wider region and the extent to which local sources contribute to pollution events. The background air upwind of London was relatively clean during both days; concentrations of CO were 88-95 ppbv, total (measured) volatile organic compounds (VOCs) were 1.6-1.8 ppbv and NOx was 0.7- 0.8 ppbv. Downwind of London, we encountered elevations in all species with CO>100 ppbv, VOCs 2.8-3.8 ppbv, CH4>2080 ppbv and NOx>4 ppbv, and peak concentrations in individual pollution events were higher still. Levels of O3 were inversely correlated with NOx during the first flight, with O3 concentrations of 37 ppbv upwind falling to 26 ppbv in the well-defined London plume. Total pollutant fluxes from London were estimated through a vertical plane downwind of the city. Our calculated CO2 fluxes are within the combined uncertainty of those estimated previously, but there was a greater disparity in our estimates of CH4 and CO. On the second day, winds were lighter and downwind O3 concentrations were elevated to 39-43 ppbv (from 32 to 35 ppbv upwind), reflecting the contribution of more aged pollution to the regional background. Elevations in pollutant concentrations were dispersed over a wider area than the first day, although we also encountered a number of clear transient enhancements from local sources. This series of flights demonstrated that even in a region of megacity outflow, such as the south-east of the UK, local fresh emissions and more distant UK sources of pollution can all contribute substantially to pollution events. In the highly complex atmosphere around a megacity where a high background level of pollution mixes with a variety of local sources at a range of spatial and temporal scales and atmospheric dynamics are further complicated by the urban heat island, the use of pollutant ratios to track and determine the ageing of air masses may not be valid. The individual sources must therefore all be well-characterised and constrained to understand air quality around megacities such as London. Research aircraft offer that capability through targeted sampling of specific sources and longitudinal studies monitoring trends in emission strength and profiles over time. © 2020 Copernicus GmbH. All rights reserved.