Forecasting the Spatial Distribution of Road-Based Transport Emissions - A Modeling Framework for Central Europe

Abstract

With transport emissions as a main contributor to global warming and regional air pollution, the spatial distribution of the emissions is important information that can help decision makers to manage health effects caused by air pollution and the climate emergency. A way to successfully distribute road-based transport emissions is to combine travel demand models with emission models, which not only enables the realistic depiction of current emission patterns, but also the forecast of future scenarios (e.g. applied for Germany in [1]). The successful implementation of this approach for central Europe is described in this study. The basis for the modeling framework is a data set with traffic loads on the road network of central European countries. These traffic loads are determined using ULTIMO, a universal transport distribution model based on open data ([2]). The main advantage of ULTIMO is that it can be set up for all European countries, producing spatially distinct traffic flows. Total transport volumes per country, determined by scenario projections as described in [3], are the basis for travel demand. Important data sources for the spatial distribution are the Open Street Map data set, which provides the road network and information about land use, and gridded population data. The determined traffic flows are then combined with emission factors from HBEFA ([4]) to compute emissions per vehicle type and road type. With the emission model HiMEMO (which is currently developed at Hereon) these emissions are spatially and temporally distributed on a grid, using the road network and population density. As a result, HiMEMO produces spatially distinct transport emissions that can serve as input for atmospheric chemistry transport models. References: [1] Matthias, V., Bieser, J., Mocanu, T., Pregger, T., Quante, M., Ramacher, M.O.P., Seum, S. & Winkler, C., Modelling road transport emissions in Germany – Current day situation and scenarios for 2040, Transportation Research Part D, 87(2020), 102536 (2020) [2] Thomsen, N., Seum, S., Using Open Data for Spatial Transport Emission Modelling, ETC 2021 (2021). [3] Seum, S., Eisenmann, C., Projecting Global Land-Base Transportation up to 2100, ETC 2020 (2020) [4] INFRAS, Handbook of Emission Factors for Road Transport HBEFA 4.1, https://www.hbefa.net/d/index.html (2019)