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NO₂-immission assessment for an urban hot-spot by modelling the emission–immission interaction

Steinhaus, Tim ; Thiem, Mikula ; Beidl, Christian (2022)
NO₂-immission assessment for an urban hot-spot by modelling the emission–immission interaction.
In: Automotive and Engine Technology, 2022, 6 (3-4)
doi: 10.26083/tuprints-00021051
Article, Secondary publication, Publisher's Version

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Abstract

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered potentially hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modelling the exact interaction remains challenging. At the same time, even lower vehicle emissions can be achieved by using synthetic fuels and the latest exhaust gas cleaning technologies. In the paper at hand, a neural network modelling approach for traffic-induced immission load is presented. On this basis, a categorization of vehicle concepts regarding their immission contribution within an impact scale is proposed. Furthermore, changes in the immission load as a result of different fleet compositions and emission factors are analysed within different scenarios. A final comparison is made as to which modification measures in the vehicle fleet offer the greatest potential for overall cleaner air.

Item Type: Article
Erschienen: 2022
Creators: Steinhaus, Tim ; Thiem, Mikula ; Beidl, Christian
Type of entry: Secondary publication
Title: NO₂-immission assessment for an urban hot-spot by modelling the emission–immission interaction
Language: English
Date: 2022
Year of primary publication: 2022
Publisher: Springer International Publishing
Journal or Publication Title: Automotive and Engine Technology
Volume of the journal: 6
Issue Number: 3-4
DOI: 10.26083/tuprints-00021051
URL / URN: https://tuprints.ulb.tu-darmstadt.de/21051
Corresponding Links:
Origin: Secondary publication service
Abstract:

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered potentially hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modelling the exact interaction remains challenging. At the same time, even lower vehicle emissions can be achieved by using synthetic fuels and the latest exhaust gas cleaning technologies. In the paper at hand, a neural network modelling approach for traffic-induced immission load is presented. On this basis, a categorization of vehicle concepts regarding their immission contribution within an impact scale is proposed. Furthermore, changes in the immission load as a result of different fleet compositions and emission factors are analysed within different scenarios. A final comparison is made as to which modification measures in the vehicle fleet offer the greatest potential for overall cleaner air.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-210516
Additional Information:

Keywords: Air quality, Zero impact, SubZero, Emission, Immission, Emission-immission-interaction, Synthetic fuel, OME

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute for Internal Combustion Engines and Powertrain Systems (VKM)
16 Department of Mechanical Engineering > Institute for Internal Combustion Engines and Powertrain Systems (VKM) > Methodik
16 Department of Mechanical Engineering > Institute for Internal Combustion Engines and Powertrain Systems (VKM) > Real Driving Emissions
Date Deposited: 25 Mar 2022 13:06
Last Modified: 28 Mar 2022 06:25
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