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Abstract: Air pollution in urban areas has important implications for health and environmental management. Consequently, various methodologies have been developed for its assessment. Traditionally, simple approaches such as the box model or the Gaussian plume have been used to assess urban air quality. The use of Eulerian grid models, which provide a more realistic and comprehensive description of the urban atmosphere, has been increasing over the past few years. This paper reports on the use of the MM5–CMAQ modelling system to predict hourly ozone levels over London, UK. This work represents the first application of MM5–CMAQ in the UK for predicting air pollution levels in London. Domain resolutions of 9km, 3km and 1km (innermost) have been employed for this study. Summer periods in July and August 2002 have been simulated and the predicted results have been compared to several urban background stations across London. Input data for emissions have been derived from the UK National Emissions inventory and from the outputs of the EMIMO emission model. The CBM-IV chemical scheme has been used to simulate the atmospheric reactions for ozone. The model performance has been evaluated with measured data through a range of statistical measures. Although, the MM5–CMAQ model reproduces the ozone temporal trends it was not able to simulate the peak magnitudes consistently. Furthermore, for nighttime the model overestimated the ozone concentrations. The paper discusses the model performance and reasons for these discrepancies in terms of the various input data including emissions and meteorological parameters. [Copyright &y& Elsevier]

Abstract: In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NO X , NO2 and O3 for London at a spatial scale of 20 m × 20 m. Model evaluation has demonstrated reasonable agreement with measurements from 80 monitoring sites in London. For NO2 the model evaluation statistics gave 73% of the hourly concentrations within a factor of two of observations, a mean bias of −4.7 ppb and normalised mean bias of −0.17, a RMSE value of 17.7 and an r value of 0.58. The equivalent results for O3 were 61% (FAC2), 2.8 ppb (MB), 0.15 (NMB), 12.1 (RMSE) and 0.64 (r). Analysis of the errors in the model predictions by hour of the week showed the need for improvements in predicting the magnitude of road transport related NO X emissions as well as the hourly emissions scaling in the model. These findings are consistent with recent evidence of UK road transport NO X emissions, reported elsewhere. The predictions of wind speed using the WRF model also influenced the model results and contributed to the daytime over prediction of NO X concentrations at the central London background site at Kensington and Chelsea. An investigation of the use of a simple NO–NO2–O3 chemistry scheme showed good performance close to road sources, and this is also consistent with previous studies. The coupling of the two models raises an issue of emissions double counting. Here, we have put forward a pragmatic solution to this problem with the result that a median double counting error of 0.42% exists across 39 roadside sites in London. Finally, whilst the model can be improved, the current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies. [Copyright &y& Elsevier]