In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NO X, NO 2 and O 3 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 NO 2 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 O 3 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-NO 2-O 3 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. © 2012 Elsevier Ltd.
Beevers, S. D., Kitwiroon, N., Williams, M. L., & Carslaw, D. C. (2012). One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions. Atmospheric Environment, 59, 47–58. https://doi.org/10.1016/j.atmosenv.2012.05.034