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 sophistication may be used to assess urban air quality and 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 (mainly based 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. Relatively simple 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, sophisticated numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy.

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Abstract:The requirements of the framework Directive on air quality assessment and management introduce real practical problems for the meteorological community. Some of the meteorological variables needed in urban air pollution assessments are not routinely measured and in normal circumstances the number of meteorological stations in urban areas is limited to a few sites often just at airports. The European wide project COST 715 on "Meteorology applied to Urban Air Pollution Problems" has been set up to review these problems. This paper describes limitations of current methods. It recognises that the urban boundary layer is a non-equilibrium situation where standard theories applied to rural areas may not be valid. It shows that predictions of concentrations for assessments should always state the uncertainty associated with them. This uncertainty can be large because of the complexity of urban pollution situations, particularly the description of the local urban meteorology. An example is given of how with some additional, local meteorological measurements, the uncertainty can be reduced. The paper concludes by listing some of the key areas where further work is required. Information about the COST 715 project is available from the Web site at

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A comprehensive ???operational??? evaluation of the performance of the Community Multiscale Air Quality (CMAQ) modelling system version 4.6 was conducted in support of pollution assessment in the UK for the calendar year 2003. The model was run on multiple grids using one-way nests down to a horizontal resolution as fine as 5 km over the whole of the UK. The model performance was evaluated for pollutants with standards and limit values (e.g. O3, PM10) and species contributing to acidic and nitrogenous deposition (e.g. NH3, SO42???, NO3???, NH4+) against data from operational national monitoring networks. The key performance characteristics of the modelling system were found to be variable according to acceptance criteria and to depend on the type (e.g. urban, rural) and location of the sites, as well as on the time of the year. As regards the techniques that were used for ???operational??? evaluation, performance generally complied with expected levels and ranged from good (e.g. O3, SO42???) to moderate (e.g. PM10, NO3???). At a few sites low correlations and large standard deviations for some species (e.g. SO2) suggest that these sites are subject to local factors (e.g. topography, emission sources) that are not well described in the model. Overall, the model tends to over predict O3 and under predict aerosol species (except SO42???). Discrepancies between predicted and observed concentrations may be due to a variety of intertwined factors, which include inaccuracies in meteorological predictions, chemical boundary conditions, temporal variability in emissions, and uncertainties in the treatment of gas and aerosol chemistry. Further work is thus required to investigate the respective contributions of such factors on the predicted concentrations

In this study the Mesoscale Model (MM5)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multiscale Air Quality (CMAQ) modelling system has been applied to a summer photochemical period in southeast England, UK. Ozone (O 3), nitrogen dioxide (NO 2) and particulate matter (PM 2.5) concentrations modelled with different horizontal grid resolutions (9 and 3 km) were evaluated against available ground-level observations from the UK Automatic Urban and Rural Network (AURN) and London Air Quality Network (LAQN) for the period of 24-28 June 2001 with a focus on O 3 predictions. This effort, which represents the first comprehensive performance evaluation of the modelling system over a UK domain, reveals that CMAQ's ability to reproduce surface O 3 observations varies with O 3 concentrations. It underpredicts O 3 mixing ratios on high-O 3 days and overpredicts the maximum and minimum hourly O 3 values for most low-O 3 days. Model sensitivity analysis with doubled anthropogenic NO x or volatile organic compounds (VOC) emissions and analysis of the daylight-averaged levels of OX (sum of O 3 and NO 2) as a function of NO x revealed that the undereprediction of peak O 3 concentrations on high-O 3 days is caused by the underprediction of regional contribution and to a lesser extent local production, which might be related to the underestimation of European emissions in EMEP inventory and the lacked reactivity of the modelled atmosphere. CMAQ systematically underpredicts hourly NO 2 mixing ratios but captures the temporal variations. The normalized mean bias for hourly NO 2 , although much larger than that for O 3 , falls well within the generally accepted range of À20% to À50%. CMAQ with both resolutions (9 and 3 km) significantly underpredicts PM 2.5 mass concentrations and fails to reproduce its temporal variations. While model performance for O 3 and PM 2.5 are not sensitive to model grid resolutions, a better agreement between modelled and measured hourly NO 2 mixing ratios was achieved with higher resolution. Further investigation into the uncertainties in

International audienceMost of the air quality modelling work has been so far oriented towards deterministic simulations of ambient pollutant concentrations. This traditional approach, which is based on the use of one selected model and one data set of discrete input values, does not reflect the uncertainties due to errors in model formulation and input data. Given the complexities of urban environments and the inherent limitations of mathematical modelling, it is unlikely that a single model based on routinely available meteorological and emission data will give satisfactory short-term predictions. In this study, different methods involving the use of more than one dispersion model, in association with different emission simulation methodologies and meteorological data sets, were explored for predicting best CO and benzene estimates, and related confidence bounds. The different approaches were tested using experimental data obtained during intensive monitoring campaigns in busy street canyons in Paris, France. Three relative simple dispersion models (STREET, OSPM and AEOLIUS) that are likely to be used for regulatory purposes were selected for this application. A sensitivity analysis was conducted in order to identify internal model parameters that might significantly affect results. Finally, a probabilistic methodology for assessing urban air quality was proposed

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