No abstract

BRT (Bus Rapid Transport) vehicles are a frequented microenvironment, it consists of exclusive lines for the transport of passengers in articulated buses. In many large cities of developing countries BRT vehicles are diesel operated buses emitting important amounts of PM2.5, a pollutant related with many health affectations. Evidence of high exposure levels have been reported onboard BRT vehicles, but detailed analysis of self-pollutions has not been developed. In this research, measurements of PM2.5 inside the BRT system of Bogota called TransMilenio were performed. Speed and location data were recorded in real-time. In-situ measurements were performed in 3 lines of the system: Av. El Dorado, Av. Caracas and Calle 80, in different seat locations inside the buses. PM2.5 concentrations above 120 µg/m3 were measured for all the cases studied. Values above the 24 h WHO (World Health Organization) recommendation were registered. Trips were determined to be between 20 to 40 minutes per passenger. A CFD (Computational Fluid Dynamics) model was implemented to simulate the exhaust emissions from the buses, 3 traffic velocities of BRT were evaluated: 20, 32 and 60 km/h. Measurements and simulation results were used to calculate the self-pollution ratios inside the vehicles. The rear of the buses was identified as the most polluted section onboard with a ratio of self-pollution about 35% average.

Bus rapid transit (BRT) vehicles are common microenvironments in urban areas. In some cities, these BRT vehicles are diesel-powered, which makes them highly pollutant. Recent studies report high levels and exposure risk to particulate matter in BRT vehicles. Nevertheless, extensive research has yet to be published, including gaseous pollutants (e.g., CO). Nevertheless, extensive research including gaseous pollutants (e.g., CO) has not been published. This research aims to evaluate the self-pollution of BRT buses in terms of exhaust gasses. For this, measurements and computational fluid dynamics (CFD) were used. Results suggest that pollutant concentrations stay low during most of the trips. However, some areas of the buses have significant swings and peaks due to the transit cycle. Here, we used CFD modeling to evaluate the dispersion of the exhaust CO inside and outside the bus. CFD results show that the bus rear has the highest concentrations, with a mean self-pollution ratio of 12%. Additionally, we developed a method based on the source-receptor relationship to quantify the impact of exhaust emissions reduction on self-pollution, showing that the technological replacement of current diesel buses would reduce self-pollution and, therefore, passenger exposure. Finally, since modeling results may be inaccurate, an uncertainty analysis was developed using the Monte Carlo method to obtain a confidence interval of 90% for the variables linked to the self-pollution.