The underestimation of Cov19 lockdown effects in modeling urban population exposure to air pollution

<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>