Title: Adding spatial flexibility to source-receptor relationships for air quality modeling
Authors: PISONI ENRICOCLAPPIER AlainDEGRAEUWE BARTTHUNIS Philippe
Citation: ENVIRONMENTAL MODELLING & SOFTWARE vol. 90 p. 68-77
Publisher: ELSEVIER SCI LTD
Publication Year: 2017
JRC N°: JRC102815
ISSN: 1364-8152
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC102815
DOI: 10.1016/j.envsoft.2017.01.001
Type: Articles in periodicals and books
Abstract: To cope with computing power limitations, air quality models that are used in integrated assessment applications are generally approximated by simpler expressions referred to as “source-receptor relationships (SRR)”. In addition to speed, it is desirable for the SRR also to be spatially flexible (application over a wide range of situations) and to require a “light setup” (based on a limited number of full Air Quality Models - AQM simulations). But “speed”, “flexibility” and “light setup” do not naturally come together and a good compromise must be ensured that preserves “accuracy”, i.e. a good comparability between SRR results and AQM. In this work we further develop a SRR methodology to better capture spatial flexibility. The updated methodology is based on a cell-to-cell relationship, in which a bell-shape function links emissions to concentrations. Maintaining a cell-to-cell relationship is shown to be the key element needed to ensure spatial flexibility, while at the same time the proposed approach to link emissions and concentrations guarantees a “light set-up” phase. Validation has been repeated on different areas and domain sizes (countries, regions, province throughout Europe) for precursors reduced independently or contemporarily. All runs showed a bias around 10% between the full AQM and the SRR. This methodology allows assessing the impact on air quality of emission scenarios applied over any given area in Europe (regions, set of regions, countries), provided that a limited number of AQM simulations are performed for training.
JRC Directorate:Energy, Transport and Climate

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