Modeling and simulation of vehicle emissions for the reduction of road traffic pollution

The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.