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Empirical modelling of a near-traffic emission hotspot – analysis of immission reduction potentials

Steinhaus, Tim ; Hartwig, Moritz ; Beidl, Christian (2022):
Empirical modelling of a near-traffic emission hotspot – analysis of immission reduction potentials. (Publisher's Version)
In: International Journal of Transport Development and Integration, 5 (4), pp. 353-366. WIT Press, ISSN 2058-8305, e-ISSN 2058-8313,
DOI: 10.26083/tuprints-00021397,
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Item Type: Article
Origin: Secondary publication service
Status: Publisher's Version
Title: Empirical modelling of a near-traffic emission hotspot – analysis of immission reduction potentials
Language: English
Abstract:

Two of the greatest challenges for future individual mobility are urban air quality and climate protection. Although a steady reduction of pollutant emissions from motor vehicles has been achieved in the past, local pollution levels within cities still reach levels that are considered hazardous to health. Although the significant contribution of road traffic to total pollution is known, especially at traffic hotspots, modelling the exact interactions remains a challenge. In this paper, a novel approach for the determination of the emission–immission interaction on the basis of a neural network model for the NO₂ immission at a near-traffic hotspot scenario is presented. In addition to a detailed description of the modelling procedure, significance analysis of the influencing variables and the interactions considered, it is also described how the specific emissions for the entire vehicle fleet are implemented in accordance with different emission standards under real driving conditions. On the basis of the model presented, achievable immission levels for currently available and future technology are investigated within scenario analysis. results show that concentrations of less than half of today’s yearly average limit values are technically feasible in hotspot situations.

Journal or Publication Title: International Journal of Transport Development and Integration
Volume of the journal: 5
Issue Number: 4
Publisher: WIT Press
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Institute for Internal Combustion Engines and Powertrain Systems (VKM)
Date Deposited: 18 May 2022 12:04
Last Modified: 18 May 2022 12:05
DOI: 10.26083/tuprints-00021397
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-213977
Additional Information:

Keywords: air pollution, emission-immission-interaction, recurrent neural networks, NO₂, NOₓ

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21397
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