Fast and Optimized Methodology to Generate Road Traffic Emission Inventories and Their Uncertainties

Abstract

Road traffic emissions are one of the main sources of air pollution in urban areas and also main sources of uncertainties in air quality numerical models. Until now, the available models for generating road traffic emission always required a lot of money, manpower and time. This inhibits decisions to preserve air quality, especially in developing countries where road traffic emissions are changing very fast. In this research, we developed a new model designed to quickly produce road traffic emissions. This model, called EMISENS, combines the well‐known top‐down and bottom‐up approaches to force them to be coherent. A Monte Carlo methodology is included for computing uncertainties. This paper presents the EMISENS model and a demonstration of its capabilities through an application over Strasbourg, France. Same input data as collected for Circul'air model using bottom‐up approach which has been applied for many years to forecast and study air pollution by the Alsatian air quality agency, are used to evaluate the impact of several simplifications. These experiments give the possibility to review older methodologies and evaluate EMISENS. We show that same average fraction of mileage driven with a cold engine can be used for all the cells of the study domain and one emission factor could replace both cold and hot emission factors.