Abstract
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.
Highlights
The percentage of the population residing in urban areas in Europe continues to increase from 74.9% in 2019; it is expected to reach 77.5% (83.7%) by 2030 (2050) [1,2]
This study describes the framework for creating emission inventories for urban air quality simulations across Europe by utilizing generic and publicly available proxies in a consistent manner
Some researchers claim that the UrbEm approach is generally applicable in European cities, there are some remaining imprecisions in the spatial distribution, e.g., the consideration of all types of airports, independent of their use or the specific location of point sources with high emissions that are not covered by the European Pollutant Release and Transfer Register (E-PRTR) emission inventory because they are below the reporting thresholds
Summary
The percentage of the population residing in urban areas in Europe continues to increase from 74.9% in 2019; it is expected to reach 77.5% (83.7%) by 2030 (2050) [1,2]. Despite past reductions in emissions that have taken place in most European countries, a significant proportion of the urban population in the EU-28 is still exposed to concentrations of certain air pollutants above the EU limit values. This is even more so when the more stringent WHO air quality guideline values are taken into account
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