Methodology for virtual prediction of vehicle-related particle emissions and their influence on ambient PM10 in an urban environment

Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Feißel, Toni;
ORCID
0000-0002-5839-1193
Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Büchner, Florian;
Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Kunze, Miles;
ORCID
0000-0001-5287-9705
Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Rost, Jonas;
ORCID
0000-0001-7252-7184
Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Ivanov, Valentin;
Zugehörigkeit
Department of Automotive Engineering, Technical University Ilmenau, Ehrenbergstraße 15, 98693 Ilmenau, Germany
Augsburg, Klaus;
ORCID
0000-0002-3118-6173
Zugehörigkeit
IAV GmbH, Rockwellstraße 3, 38518 Gifhorn, Germany
Hesse, David;
ORCID
0000-0003-1284-4385
Zugehörigkeit
Audi AG, Auto-Union-Str. 1, 85057 Ingolstadt, Germany
Gramstat, Sebastian

As a result of rising environmental awareness, vehicle-related emissions such as particulate matter are subject to increasing criticism. The air pollution in urban areas is especially linked to health risks. The connection between vehicle-related particle emissions and ambient air quality is highly complex. Therefore, a methodology is presented to evaluate the influence of different vehicle-related sources such as exhaust particles, brake wear and tire and road wear particles (TRWP) on ambient particulate matter (PM). In a first step, particle measurements were conducted based on field trials with an instrumented vehicle to determine the main influence parameters for each emission source. Afterwards, a simplified approach for a qualitative prediction of vehicle-related particle emissions is derived. In a next step, a virtual inner-city scenario is set up. This includes a vehicle simulation environment for predicting the local emission hot spots as well as a computational fluid dynamics model (CFD) to account for particle dispersion in the environment. This methodology allows for the investigation of emissions pathways from the point of generation up to the point of their emission potential.

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