Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.
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.
Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.
<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, Li&#232;ge - 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), Li&#232;ge (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>
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