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Hourly roadside traffic emissions from bottom-up inventory for the city of Berlin

Urheber*innen
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Chan,  Edward
IASS Institute for Advanced Sustainability Studies Potsdam;

/persons/resource/494

Leitao,  Joana
IASS Institute for Advanced Sustainability Studies Potsdam;

/persons/resource/431

Schmitz,  Sean
IASS Institute for Advanced Sustainability Studies Potsdam;

/persons/resource/19

Butler,  Tim M.
IASS Institute for Advanced Sustainability Studies Potsdam;

Kerschbaumer,  A.
External Organizations;

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Zitation

Chan, E., Leitao, J., Schmitz, S., Butler, T. M., Kerschbaumer, A. (2022): Hourly roadside traffic emissions from bottom-up inventory for the city of Berlin - Proceedings and presentations, 21st International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, HARMO 2022 (Aveiro 2022).


Zitierlink: https://publications.rifs-potsdam.de/pubman/item/item_6002659
Zusammenfassung
Emissions of nitrogen oxides (NOx) and particulate matter (PM) from traffic sources are modelled for the City of Berlin with a bottom-up approach using HBEFA emission factors. Road network topology, vehicle fleet distribution, traffic activities, as well as meteorological conditions are used in tandem for generating hourly emissions at road segment resolutions. Aggregated annual daily mean emissions are presented and have been shown to be consistent with officially reported inventory values. Meanwhile, street level hourly emission data for different day types (i.e., workdays, Fridays, Saturdays, and Sundays/Holidays) are generated to coincide with local conditions representing recent observational campaigns using low-cost sensors (LCSs). These locations reflect different road types, where corresponding influences of traffic volume and traffic flow state on the emission output are evident. The results from this exercise provide high-resolution boundary conditions for future meso- and urban scale model evaluation studies, or further as a starting point for exposure assessment to traffic pollutants under different traffic activity scenarios.