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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.
In: International Journal of Transport Development and Integration, 5 (4)
doi: 10.26083/tuprints-00021397
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Empirical modelling of a near-traffic emission hotspot – analysis of immission reduction potentials
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: WIT Press
Journal or Publication Title: International Journal of Transport Development and Integration
Volume of the journal: 5
Issue Number: 4
DOI: 10.26083/tuprints-00021397
Corresponding Links:
Origin: Secondary publication service
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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-213977
Additional Information:

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

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Internal Combustion Engines and Powertrain Systems (VKM)
Date Deposited: 18 May 2022 12:04
Last Modified: 14 Dec 2022 07:06
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21397
PPN: 502531002
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