Traffic emission estimation based on quasi-dynamic network loading

Highly congested urban areas is a side effect of our modern society. Traffic congestion increases travel times, but also implies increased energy usage and vehicular emissions, having a negative impact on both air quality and climate change. According to the European Environment Agency, transportation, and especially exhaust emissions from road traffic, remains a significant contributor to the main air pollutants affecting substantially the urban air quality [EEA, 2016]. Specifically, NOx, CO and PM2.5 make up 32%, 23% and 8% of the total emissions, respectively. Considering the high rates of road traffic emissions and the negative effects on air pollution, one can clearly conclude that this type of emissions significantly affects human health. In order to evaluate this effect, the estimation of the magnitude, location and the duration of the exposure to the pollutant is necessary. The modelling components, which are necessary to move from road emissions to the estimation of the socio-economic impact of health effects, are described in Figure 1. Initially, the amount of a pollutant emitted at a street level in grams per space and time unit is estimated by emission modelling. Next, a dispersion model can be used to provide the concentration of a pollutant in grams per m at a specific location and time period. Dispersion models describe the chemical and physical processes within a plume combining the emission rates estimated by the emission model, with some geographical, meteorological and background pollution information. Then, exposure modelling, considering the spatio-temporal information on the pollutant’s concentrations as well as demographics and land use data, estimates the final number of people inhaling the pollutant per time unit. The magnitude of the combined effects on health and environment, determine the economic effects of air pollution [Smit et al., 2010].