The lockdown measures implemented worldwide to slow the spread of the COVID–19 pandemic have allowed for a unique real–world experiment, regarding the impacts of drastic emission cutbacks on urban air quality. In this study we assess the effects of a 7–week (23 March–10 May, 2020) lockdown in the Greater Area of Athens, coupling in situ observations with estimations from a meteorology–atmospheric chemistry model. Measurements in central Athens during the lockdown were compared with levels during the pre– and post–lockdown 3–week periods and with respective levels in the four previous years. We examined regulatory pollutants as well as CO2, black carbon (BC) and source–specific BC components. Models were run for pre–lockdown and lockdown periods, under baseline and reduced–emissions scenarios. The in–situ results indicate mean concentration reductions of 30–35% for traffic–related pollutants in Athens (NO2, CO, BC from fossil fuel combustion), compared to the pre–lockdown period. A large reduction (53%) was observed also for the urban CO2 enhancement while the reduction for PM2.5 was subtler (18%). Significant reductions were also observed when comparing the 2020 lockdown period with past years. However, levels rebounded immediately following the lift of the general lockdown. The decrease in measured NO2 concentrations was reproduced by the implementation of the city scale model, under a realistic reduced–emissions scenario for the lockdown period, anchored at a 46% decline of road transport activity. The model permitted the assessment of air quality improvements on a spatial scale, indicating that NO2 mean concentration reductions in areas of the Athens basin reached up to 50%. The findings suggest a potential for local traffic management strategies to reduce ambient exposure and to minimize exceedances of air quality standards for primary pollutants.

As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO2 predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites.

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Long-term nitrogen dioxide (NO2) slant column density measurements using the MAX-DOAS (multi-axis differential optical absorption spectroscopy) technique were analyzed in order to demonstrate the temporal and horizontal variability of the trace gas in Athens for the period October 2012–July 2017. The synergy with in situ measurements and model simulations was exploited for verifying the MAX-DOAS technique and its ability to assess the spatiotemporal characteristics of NO2 pollution in the city. Tropospheric NO2 columns derived from ground-based MAX-DOAS observations in two horizontal and five vertical viewing directions were compared with in situ chemiluminescence measurements representative of urban, urban background and suburban conditions; a satisfactory correlation was found for the urban (r ≈ 0.55) and remote areas (r ≈ 0.40). Mean tropospheric slant columns retrieved from measurements at the lowest elevation over the urban area ranged from 0.1 to 32 × 1016 molec cm−2. The interannual variability showed a rate of increase of 0.3 × 1016 molec cm−2 per year since 2012 in the urban area, leading to a total increase of 20%. The retrieved annual cycles captured the seasonal variability with lower NO2 levels in summer, highly correlated (r ≈ 0.85) with the urban background and suburban in situ observations. The NO2 diurnal variation for different seasons exhibited varied patterns, indicating the different role of photochemistry and anthropogenic activities in the different seasons. Compared to in situ observations, the MAX-DOAS NO2 morning peak occurred with a one-hour delay and decayed less steeply in winter. Measurements at different elevation angles are shown as a primary indicator of the vertical distribution of NO2 at the urban environment; the vertical convection of the polluted air masses and the enhanced NO2 near-surface concentrations are demonstrated by this analysis. The inhomogeneity of the NO2 spatial distribution was shown using a relevant inhomogeneity index; greater variability was found during the summer period. Comparisons with city-scale model simulations demonstrated that the horizontal light path length of MAX-DOAS covered a distance of 15 km. An estimation of urban sources’ contribution was also made by applying two simple methodologies on the MAX-DOAS measurements. The results were compared to NO2 predictions from the high resolution air quality model to infer the importance of vehicle emissions for the urban NO2 levels; 20–35% of the urban NO2 was found to be associated with road transport.