A novel methodology for interpreting air quality measurements from urban streets using CFD modelling

摘要

In this study, a novel computational fluid dynamics (CFD) based methodology has been developed to interpret long-term averaged measurements of pollutant concentrations collected at roadside locations. The methodology is applied to the analysis of pollutant dispersion in Stratford Road (SR), a busy street canyon in Birmingham (UK), where a one-year sampling campaign was carried out between August 2005 and July 2006. Firstly, a number of dispersion scenarios are defined by combining sets of synoptic wind velocity and direction. Assuming neutral atmospheric stability, CFD simulations are conducted for all the scenarios, by applying the standard k-s turbulence model, with the aim of creating a database of normalised pollutant concentrations at specific locations within the street. Modelled concentration for all wind scenarios were compared with hourly observed NOX data. In order to compare with long-term averaged measurements, a weighted average of the CFD-calculated concentration fields was derived, with the weighting coefficients being proportional to the frequency of each scenario observed during the examined period (either monthly or annually). In summary the methodology consists of (i) identifying the main dispersion scenarios for the street based on wind speed and directions data, (ii) creating a database of CFD-calculated concentration fields for the identified dispersion scenarios, and (iii) combining the CFD results based on the frequency of occurrence of each dispersion scenario during the examined period. The methodology has been applied to calculate monthly and annually averaged benzene concentration at several locations within the street canyon so that a direct comparison with observations could be made. The results of this study indicate that, within the simplifying assumption of non-buoyant flow, CFD modelling can aid understanding of long-term air quality measurements, and help assessing the representativeness of monitoring locations for population exposure studies.