Articles | Volume 13, issue 11
Geosci. Model Dev., 13, 5725–5736, 2020
https://doi.org/10.5194/gmd-13-5725-2020
Geosci. Model Dev., 13, 5725–5736, 2020
https://doi.org/10.5194/gmd-13-5725-2020

Methods for assessment of models 23 Nov 2020

Methods for assessment of models | 23 Nov 2020

Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses?

Bart Degraeuwe et al.

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Cited articles

Carnevale, C., Finzi, G., Pisoni, E., and Volta, M.: Neuro-fuzzy and neural network systems for air quality control, Atmos. Environ., 43, 4811–4821, 2009. 
Clappier, A., Pisoni, E., and Thunis, P.: A new approach to design source-receptor relationships for air quality modelling, Environ. Modell. Softw., 74, 66–74, 2015. 
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Short summary
To make decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.