Challenges in traffic emission modeling and their application

Abstract

Atmospheric chemistry transport models (CTMs) are since long time an important tool for studying multi-phase chemical reactions, particle formation and deposition processes of numerous trace gases and primary particles emitted into the atmosphere. These models need emission and meteorological information as essential inputs. Emissions are more than just a static inventory, they have dynamical spatial and temporal components. Therefore, separate model systems are typically applied for calculating the CTM input data. Their development and comprehensive evaluation are essential for enabling progress in air quality modelling. Emission control politics, in addition, need well suited tools to assess the overall impact of often costly emission reduction measures beforehand. This contribution focuses on emission models for ground based transport, in particular for ship traffic and road traffic. While state-of-the-art ship emission models apply bottom-up approaches that are based on ship position data and technical ship characteristics, road traffic emission models cannot treat each vehicle individually. Nevertheless, road traffic activity data can also be combined with emission factors for certain vehicle types and emission standards. However, diurnal profiles and weekday dependencies are often not included in the activities. How highly resolved traffic activity data from mobile phones can be used was demonstrated during the lockdowns in the early phase of the COVID-19 pandemic. Google mobility data or Apple data was widely used to improve road traffic emission estimates in spring 2020. In the presentation, challenges and limitations of ground based traffic emission model systems are discussed. In addition, their power for improving air quality simulations as well as for constructing consistent future emission scenarios, that are essential for intelligent emission reduction policies, are illustrated.