Strong ionospheric perturbations were generated by the intense geomagnetic storm on 17 March 2015. In this article, we are studying perturbations in the European‐African sector observed in the total electron content (TEC). Focal points are wavelike phenomena considered as large‐scale traveling ionospheric disturbances (LSTIDs). In the European‐African sector, the storm produced three different types of LSTIDs: (1) a concurrent TEC perturbation at all latitudes simultaneously; (2) one LSTID propagating toward the equator, having very large wave parameters (wavelength: ≈3600 km, period: ≈120 min, and speed: ≈500 m/s); and (3) several LSTIDs propagating toward the equator with typical wave parameters (wavelength: ≈2100 km, period: ≈60 min, and speed ≈600 m/s). The third type of LSTIDs is considered to be exited as most LSTIDs either due to variations in the Joule heating or variations in the Lorentz force, whereas the first two perturbation types are rather unusual in their appearance. They occurred during the partial recovery phase when the geomagnetic perturbations were minor and the interplanetary magnetic field turned northward. A westward prompt penetration electric field is considered to excite the first perturbation signature, which indicates a sudden TEC depletion. For the second LSTID type, variations in the Lorentz force because of perturbed electric fields and a minor particle precipitation effect are extracted as possible excitation mechanisms.

Abstract. Estimating the impact of ship emissions on local air quality is a topic of high relevance, especially in large harbor cities. For chemistry-transport modeling studies, the initial plume rise and dispersion play a crucial role for the distribution of pollutants into vertical model layers. This study aims at parameterizing the vertical downward dispersion in the near field of a prototype cruise ship, depending on several meteorological and technical input parameters. By using the microscale chemistry, transport and stream model (MITRAS), a parameterization scheme was developed to calculate the downward dispersion, i.e., the fraction of emissions, which will be dispersed below stack height. This represents the local concentration in the vicinity of the ship. Cases with and without considering the obstacle effect of the ship have been compared. Wind speed and ship size were found to be the strongest factors influencing the downward dispersion, which can reach values up to 55 % at high wind speed and lateral wind. This compares to 31 % in the case where the obstacle effect was not considered and shows the importance of obstacle effects when assessing the ground-level pollution situation in ports.

Abstract. The lockdown measures taken to prevent a rapid spreading of the coronavirus in Europe in spring 2020 led to large emission reductions, particularly in road traffic and aviation. Atmospheric concentrations of NO2 and PM2.5 were mostly reduced when compared to observations taken for the same time period in previous years; however, concentration reductions may not only be caused by emission reductions but also by specific weather situations. In order to identify the role of emission reductions and the meteorological situation for air quality improvements in central Europe, the meteorology chemistry transport model system COSMO-CLM/CMAQ was applied to Europe for the period 1 January to 30 June 2020. Emission data for 2020 were extrapolated from most recent reported emission data, and lockdown adjustment factors were computed from reported activity data changes, e.g. Google mobility reports. Meteorological factors were investigated through additional simulations with meteorological data from previous years. The results showed that lockdown effects varied significantly among countries and were most prominent for NO2 concentrations in urban areas with 2-week-average reductions up to 55 % in the second half of March. Ozone concentrations were less strongly influenced (up to ±15 %) and showed both increasing and decreasing concentrations due to lockdown measures. This depended strongly on the meteorological situation and on the NOx / VOC emission ratio. PM2.5 revealed 2 %–12 % reductions of 2-week-average concentrations in March and April, which is much less than a different weather situation could cause. Unusually low PM2.5 concentrations as observed in northern central Europe were only marginally caused by lockdown effects. The lockdown can be seen as a big experiment about air quality improvements that can be achieved through drastic traffic emission reductions. From this investigation, it can be concluded that NO2 concentrations can be largely reduced, but effects on annual average values are small when the measures last only a few weeks. Secondary pollutants like ozone and PM2.5 depend more strongly on weather conditions and show a limited response to emission changes in single sectors.

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Abstract. Estimating the impact of ship emissions on local air quality is a topic of high relevance, especially in large harbour cities. For chemistry transport modeling studies, the initial plume rise and dispersion play a crucial role for the distribution of pollutants into vertical model layers. This study aims at parameterizing the vertical downward dispersion in the near-field of a prototype cruise ship, depending on several meteorological and technical input parameters. By using the micro-scale transport and stream model MITRAS, a parameterization scheme was developed to calculate the downward dispersion, i.e. the fraction of emissions, which will be dispersed below stack height. This represents the local concentration in the vicinity of the ship. Cases with and without considering the obstacle effect of the ship have been compared. Wind speed and ship size were found to be the strongest factors influencing the downward dispersion, which can reach values up to 55 % at high wind speed and lateral wind. This compares to 31 % in the case where the obstacle effect was not considered and shows the importance of obstacle effects when assessing the ground-level pollution situation in ports.

Abstract. The modeling of ship emissions in port areas involves several uncertainties and approximations. In Eulerian grid models, the vertical distribution of emissions plays a decisive role for the ground-level pollutant concentration. In this study, model results of a microscale model, which takes thermal plume rise and turbulence into account, are derived for the parameterization of vertical ship exhaust plume distributions. This is done considering various meteorological and ship-technical conditions. The influence of three different approximated parameterizations (Gaussian distribution, single-cell emission and exponential Gaussian distribution) on the ground-level concentration are then evaluated in a city-scale model. Choosing a Gaussian distribution is particularly suitable for high wind speeds (>5 m s−1) and a stable atmosphere, while at low wind speeds or unstable atmospheric conditions the plume rise can be more closely approximated by an exponential Gaussian distribution. While Gaussian and exponential Gaussian distributions lead to ground-level concentration maxima close to the source, with single-cell emission assumptions the maxima ground-level concentration occurs at a distance of about 1500 m from the source. Particularly high-resolution city-scale studies should therefore consider ship emissions with a suitable Gaussian or exponential Gaussian distribution. From a distance of around 4 km, the selected initial distribution no longer shows significant differences for the pollutant concentration near the ground; therefore, model studies with lower resolution can reasonably approximate ship plumes with a single-cell emission.

<p><strong>Summary</strong></p><p>This study aims to quantify the combined effect of changing emissions and population activity in the estimation of urban population during the first COVID19-lockdown measures in the beginning of the year 2020. While most studies focus on the impact of changing emissions in concentration reductions due to lockdown measures, we identified the additional change in population exposure for three different cities in Europe, when taking into account the change in population activity in a dynamic urban population exposure model. The results show that population exposure is underestimated by up to 8% for NO<sub>2</sub> and by up to 29% for PM<sub>2.5</sub> exposure, when neglecting the change in population activity.</p><p><strong>Introduction</strong></p><p>The lockdown response to the coronavirus disease 2019 (COVID-19) has caused an exceptional reduction in global economic and transport activity. Many recent measurement and modelling studies tested the hypothesis that this has reduced ground-level air pollution concentrations as well as the associated population exposure and health effects, especially in urban areas. Although Google and Apple mobility data is utilized in such air quality modelling studies to derive changes in emissions, the mobility data is not used to reflect changes in population activity patterns. Nevertheless, neglecting the mobility of populations in exposure estimates is known to introduce substantial BIAS; especially on urban-scales. Therefore, we identified the additional change in population exposure for three different cities in Europe (Hamburg - DE, Liège - BE, Marseille - FR), when taking into account the change in population activity in a dynamic urban population exposure model.</p><p><strong><span>Methods</span></strong></p><p><span>To model the impact of (1) changing emissions and (2) the change in population activity patterns in our multi-city exposure study, we applied mobility data as derived from different sources (Google, Eurostat, Automatic Identification System, etc.). The aim is to quantify the BIAS in air pollution (PM<sub>2.5</sub>, NO<sub>2</sub>) exposure estimates that arises from neglecting population activity under COVID-19 lockdown conditions. We applied the urban-scale chemistry transport model EPISODE-CityChem (Karl et. al 2019) and the urban dynamic exposure model UNDYNE (Ramacher et al. 2020) in the European cities Marseille (FR), Liège (BE) and Hamburg (DE) in the first six months of 2020. Based on flexible microenvironment definitions for different surroundings (based on the Copernicus UrbanAtlas) and modes of transport (based on OpenStreetMap), the UNDYNE model allows for a flexible application of population activity in European urban areas. This feature was used to evaluate and compare a set of emission and activity scenarios.</span></p><p><strong><span>Results</span></strong></p><p><span>Compared to non-lockdown conditions, the derived lockdown activity profiles showed substantial additional changes in the total exposure of the urban population in all cities with up to 8% for NO<sub>2</sub> and by up to 29% for PM<sub>2.5</sub>. The analysis of estimated exposure in the different microenvironments home, work and transport reflects the changes in population activity with increasing exposure in the home environment and decreasing exposure in the work and transport environments. Due to the general high reduction of population exposure in transport activities, a significant change of exposure for different modes of transport was not observed.</span></p>

<p class="western">Die Emissionsänderungen aufgrund des COVID-19 Lockdowns stellten zweifellos die stärkste Reduktion seit Jahrzehnten dar. Besonders betroffen waren die Bereiche Straßen- und Luftverkehr, aber auch die Bereiche Industrie und Energieerzeugung. Durch Rekonstruktion der Emissionsänderungen und unter Zuhilfenahme von Chemietransportrechnungen lässt sich der Einfluss des Lockdowns auf verschiedene Luftschadstoffe systematisch quantifizieren. Dies ist in einigen Studien für die europäische Region schon diskutiert worden. In unserem Beitrag wollen wir allerdings untersuchen, inwiefern meteorologische Bedingungen die Lockdown-Phase beeinflusst haben.</p> <p class="western">Für die Berechnung der Luftchemie und der aerosolphysikalischen Prozesse greifen wir auf das Chemietransportmodell CMAQ zurück, welches vom globalen IFS-CAMS Modell angetrieben wird und mit einem Mehrfachnesting die zentraleuropäische Region simuliert. Die Emissionen werden auf Grundlage des Datensatzes CAMS REG-AP-EU (verfügbar auf der ECCAD-Webseite) erstellt. Dabei wird ein Basis-Emissionszenario (normale Emissionen ohne Lockdown Effekte, ‚noCov‘) für das Jahr 2020 rekonstruiert und ebenso ein Lockdown-Szenario, welches anhand verschiedener Indikatoren (u.a. google mobility reports, air traffic reports) den lockdownbedingten Emissionsrückgang abbildet. Zusätzlich zum Emissionsszenario wird in unserer Untersuchung die Lockdown-Situation in solchen Jahren nachgestellt, die sich atmosphärisch vom Jahr 2020 unterscheiden. </p> <p class="western">Wir analysieren die zeitliche und räumliche Verteilung der Konzentrationen von Stickstoffverbindungen, Ozon und PM2.5 und bewerten den Einfluss der atmosphärischen Dynamik auf die Luftchemie. Diese Vorgehensweise ist deshalb von Bedeutung, weil sich nicht alle simulierten bzw. beobachteten Konzentrationsfelder immer allein durch den Einfluss einer Emissionsminderung (COVID) erklären lassen.</p>