Evaluation of aggregate and individual vehicle activity on emissions models and their impact on air quality

Abstract

Near-roadway air quality modelling requires detailed inputs about traffic, vehicle emissions, and atmospheric dispersion in order to capture microscopic features of each phenomena. However, these inputs are often a major source of uncertainty in resultant air quality estimates. We aim to evaluate the impact of different traffic behavior patterns on vehicle emissions and the subsequent effect of these emissions on air quality estimates derived from dispersion models. Two approaches were developed, an “Aggregate” approach, where the behavior of ensembles of vehicles were simulated, and an “Instantaneous” approach, where individual vehicles were tracked through the network. We also developed a dynamic integrated approach to include traffic patterns, vehicle-induced emissions, and air dispersion, and compared the contribution of each to air quality simulations. Our results indicate that the Aggregate approach tends to overestimate emissions. Validation of NO2 concentrations generated from the dispersion model against measurements revealed a correlation of 61% and 79% for the Aggregate and Instantaneous approaches, respectively. Compared to the more commonly used Aggregate approach, modelling the individual behavior of vehicles provides a marked improvement on emissions and subsequent air quality estimates. Development of correction coefficients could improve Aggregate emission models based on the Instantaneous approach under different traffic situations.