Modelling air quality in street canyons: a review

High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air qualityand support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition–resuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models. A range of monitoring techniques is used to measure pollutant concentrations in urban streets. Point measurement methods (continuous monitoring, passive and active pre-concentration sampling, grab sampling) are available for gaseous pollutants. A number of sampling techniques (mainlybased on filtration and impaction) can be used to obtain mass concentration, size distribution and chemical composition of particles. A combination of different sampling/monitoring techniques is often adopted in experimental studies. Relativelysimple mathematical models have usually been used in association with field measurements to obtain and interpret time series of pollutant concentrations at a limited number of receptor locations in street canyons. On the other hand, advanced numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy.

Performance measurement and benchmarking of a major innovation programme

Purpose: The purpose of this paper is to describe the problems encountered and the solutions developed when using benchmarking and key performance indicators (KPIs) to monitor a major UK social house building innovation (change) programme. The innovation programme sought improvements to both the quality of the house product and the procurement process. Design/methodology/approach: Benchmarking and KPIs were used to quantify performance and in-depth case studies to identify underlying cause and effect relationships within the innovation programme. Findings: The inherent competition between consortium members; the complexity of the relationship between the consortium and its strategic partner; the lack of an authoritative management control structure; and the rapidly changing nature of the UK social housing market all proved problematic to the development of a reliable and robust monitoring system. These problems were overcome by the development of multi-dimensional benchmarking model that balanced the needs and aspirations of the individual organisations with the broader objectives of the consortium. Research limitations/implications: Whilst the research methodology provides insight into the factors that affected the performance of a major innovation programme its findings may not be representative of all projects. Practical implications: The lessons learnt should assist those developing benchmarking models for multi-client consortia. Originality/value: The work reported in this paper describes an inclusive approach to benchmarking in which a multiple client group and their strategic partner sought to work together for shared gain. Very few papers have addressed this issue.