Influence of virtual changes in building configurations of a real street canyon on the dispersion of PM10

A review of factors impacting exposure to PM2.5, ultrafine particles and black carbon in Asian transport microenvironments

“…Wind direction is another important factor, which becomes significant when it comes to street canyons. For example, depending on relationships between street canyon height and width (i.e., aspect ratios), a vortex generated within a street canyon can push pollutants from the windward to the leeward side of the street canyon (Kumar et al, 2009;Kumar et al, 2011b;Vardoulakis et al, 2003;Garcia et al, 2013) resulting in increased exposure for pedestrians and cyclists (Kaur et al, 2007). Ambient, near-road, and TME pollutant concentrations tend to vary seasonally, with UFP typically highest in cold months when condensation of gases into liquids is greatest and PM 2.5 is higher in spring and summer due to the secondary aerosol formation (Kaur et al, 2006).…”

A Computational Fluid Dynamic (CFD) Simulation of PM10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model

“…However, several works of research focusing on the gaps indicates that a crossroad gap between buildings led to a significantly different wind profile inside the canyon [ 36 ]. It takes a bit longer to dilute particles during the cross canyon winds in a real street canyon model due to such a gap between buildings [ 36 , 39 ].…”

“…Existing studies predominately have focused on the street canyon configuration, such as the aspect ratio and height ratio, in order to examine the impact of street geometry on the flow structure, the vortexes and the pollutant dispersion [ 19 , 33 , 34 , 35 , 38 , 39 ]. Most of these studies are based on idealized modeling in a CFD approach, overlooking factors that exist in real street canyons, such as irregular streets and the existing gaps in adjacent buildings [ 34 , 43 ].…”

“…Furthermore, as regards the particle dispersion in street canyon, most research has taken traffic as the example. In order to predict the main emissions source, the traffic-based emission model, such as the ADMS-Urban model, is used as emission factor, which covers the traffic factors, including the number of vehicles per hour, vehicle types and average vehicle speed [ 39 ]. While particles from construction activity are deduced from earthworks and road works, the bases of emission factors are quite different [ 17 ].…”

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models

“…Therefore a need remains to measure them at a greater number of locations or model them using detailed modelling systems such as CFD [12] or numerical models [13]. The monitoring methods include acquisition of PM 10 concentrations using the scientific instruments while the numerical simulation of pollutants dispersion using computational tools, physical modelling using wind tunnel experiments [14], or through the statistical methods [15,16].…”