Articles | Volume 16, issue 4
Model description paper
02 Mar 2023
Model description paper |  | 02 Mar 2023

Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions

Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler

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Cited articles

Berlin City Senate: Luftreinhalteplan für Berlin: 2. Fortschreibung. Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, 194–195, 2019. 
Buch, N., Velastin, S. A., and Orwell, J.: A review of computer vision techniques for the analysis of urban traffic, IEEE T. Intell. Transp., 12, 920–939, 2011.  
Builtjes, P. J. H., van Loon, M., Schaap, M., Teeuwisse, S., Visschedijnk, A. J. H., and Bloos, J. P.: Project on the modelling and verification of ozone reduction strategies: contribution of TNO-MEP, TNO-report MEP-R2003/166, ISSN: 1875-2322, 2003. 
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Chan, E. C. and Butler, T. M.: urbanChemFoam 1.0: large-eddy simulation of non-stationary chemical transport of traffic emissions in an idealized street canyon, Geosci. Model Dev., 14, 4555–4572,, 2021. 
Short summary
Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.