UrbEm - The Urban Emission downscaling model for air quality modeling
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1.
Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research
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2.
National Observatory of Athens
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1.
National Observatory of Athens
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2.
Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research
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3.
Netherlands Organisation for Applied Scientific Research
Description
Purpose
The use of regional emission inventories can be challenging for urban-scale AQ applications and air quality management in cities. Nevertheless, their exploitation through disaggregation by utilizing spatial proxies is a credible solution for European cities that lack bottom-up emission inventories.
To this end, we developed the UrbEm approach, which enables in a modular manner downscaling of gridded regional emissions with specific spatial proxies based on a variety of open access, robust, sustainable and frequently updated sources. UrbEm can be applied to any urban area in Europe and provides methodological homogeneity between different cities.
To demonstrate the general applicability and performance of the developed method and tool, we introduced the method, and compared the spatial distribution of uniformly disaggregated regional emissions with emissions downscaled with the UrbEm approach for the differing cities of Athens and Hamburg (https://www.mdpi.com/2073-4433/12/11/1404).
Access
The UrbEm downscaling approach is completely free of cost and open source. Its application is realized in (1) a series of R scripts and (2) as Pyhton script. Both applications rely on e.g. CAMS-REG emission inventories as well as a set (maps) of spatial proxies, which need to be downloaded before using UrbEm. UrbEm v1.0.0 can be accessed via GitHub (https://github.com/martinottopaul/UrbEm), while the required spatial datasets are published via Zenodo (https://zenodo.org/record/5508739).
Funding
The authors acknowledge the funding received by ERA-PLANET (www.era-planet.eu, accessed: 23 October 2021), trans-national project SMURBS (www.smurbs.eu, accessed: 23 October 2021) (Grant Agreement n. 689443), funded under the EU Horizon 2020 Framework Programme.