A surrogate approach for estimating vehicle-related emissions under heterogenous traffic conditions.

Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. In this study, we develop a surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates. The input variables are obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.

On-Road NO and Smoke Emissions of Diesel Light Commercial Vehicles-Combining Remote Sensing Measurements from across Europe.

Light commercial vehicles (LCVs) account for about 10-15% of road traffic in Europe. There have only been few investigations on their on-road emission performance. Here, on-road remote sensing vehicle emission measurements from 18 locations across four European countries are combined for a comprehensive analysis of NO and smoke emission rates from diesel LCV in the past two decades.