Integrated Modeling Framework for Highway Traffic Pollution Estimation and Dispersion

Abstract

Air quality is of great concern to the public. Airborne pollutants, such as carbon monoxide (CO), carbon dioxide (CO₂) and Particulate Matter (PM), negatively impact people’s living conditions. In particular, vehicular exhaust is a major source of these pollutants. Extensive previous research has contributed to modeling traffic pollutant emission. Popular approaches include computational simulation-based methods, which have been dominant in studies related to traffic emissions. However, simulation-based methods may not reflect changes in traffic and environmental factors in a real world situation. In order to address this and other limitations, our study employs real world traffic, meteorological and geographical data. In order to fully utilize this data and apply it to traffic pollutant emission and dispersion modeling, this study proposes a framework to integrate a traffic emission model, dispersion model and multi-source public data to best estimate traffic emissions and dispersion. The framework is based on the concept that traffic sensors + meteorology sensors = emissions sensors. The results from case studies show that the concept is feasible and that air pollutants can be estimated over a large area. Sensitivity analyses on atmosphere stability and surface roughness showed not only the effectiveness of the proposed framework, but also demonstrated potential applications for inter-disciplinary research including public health and land-use planning.