Empirical Modelling of A Near-Traffic Emission Hotspot – Analysis of Immission Reduction Potentials

Empirical Modelling of A Near-Traffic Emission Hotspot – Analysis of Immission Reduction Potentials

Tim Steinhaus Moritz Hartwig Christian Beidl

Institute for Internal Combustion Engines and Powertrain Systems, Technical University of Darmstadt, Germany

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© 2021 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).



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.


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


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