Preprints
https://doi.org/10.5194/gmd-2022-147
https://doi.org/10.5194/gmd-2022-147
Submitted as: model description paper
24 Jun 2022
Submitted as: model description paper | 24 Jun 2022
Status: this preprint is currently under review for the journal GMD.

Yeti 1.0: a generalized framework for constructing bottom-up emission inventory from traffic sources

Edward C. Chan1, Joana Leitão1, Andreas Kerschbaumer2, and Timothy M. Butler1 Edward C. Chan et al.
  • 1Institute for Advanced Sustainability Studies, Potsdam, Germany
  • 2Senatsverwaltung für Umwelt, Mobilität, Verbraucher- und Klimaschutz, Berlin, Germany

Abstract. This paper outlines the development and operation of Yeti, a bottom-up traffic emission inventory framework written in the Python 3 scripting language. A generalized representation of traffic activity and emission data affords a high degree of scalability and flexibility in the use and execution of Yeti, while accommodating a wide range of details on topological, traffic, and meteorological data. The resulting traffic emission data are calculated at a road level resolution on an hourly basis. Yeti is initially applied to traffic activity and fleet composition data provided by the Senate Administration for the City of Berlin, which serves as the region of interest, where the Yeti calculated emissions are highly consistent with officially reported annual aggregate levels, broken down according to different exhaust and non-exhaust emission modes. Diurnal emission profiles on select road segments show not only the dependence from traffic activities, but also from road type and meteorology. These road level emissions are further classified on the basis of vehicle categories and Euro emission classes, and the results obtained confirmed the observations of the City of Berlin and subsequent rectifications.

Edward C. Chan et al.

Status: open (until 11 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-147', Sergio Ibarra, 18 Jul 2022 reply

Edward C. Chan et al.

Model code and software

Source code for Yeti 1.0: a generalized framework for constructing bottom-up emission inventory from traffic sources E. C. Chan; J. Leitão; A. Kerschbaumer; T. M. Butler https://doi.org/10.5281/zenodo.6594260

Edward C. Chan et al.

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Short summary
Yeti is a HBEFA-based traffic emissions 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. As a result, 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.