Use of a new generation urban scale dispersion model to estimate the concentration of oxides of nitrogen and sulphur dioxide in a large urban area

This paper describes the use of urban emission inventory data and an urban scale dispersion model ADMS-Urban. to calculate concentrations of NOx and SO2 , in two areas of London, Central London and East London. Local authorities in the UK are expected to undertake reviews of air quality in their areas to determine whether air quality objectives set by the National Air Quality Strategy will be achieved by 2005. The UK Government proposes that local authorities in urban areas develop spatially disaggregated emission inventories in conjunction with dispersion models to assess compliance with the air quality objectives laid down in the strategy. This paper examines the performance of an urban emission inventory and a dispersion model ADMS-Urban. to assess air quality from emission sources by comparing model predictions with monitored concentrations at four locations. The model used has a GIS interface and uses a spatially disaggregated urban emissions inventory to provide an integrated emission inventory and dispersion modelling system. The dispersion model used in this study is a second-generation Gaussian dispersion model which is characterised by the use of boundary layer similarity profiles to parameterise the variation of turbulence with height within the boundary layer. In a large urban area such as London there are many different sources contributing to the concentrations in the atmosphere. This modelling study aims to examine and evaluate the consideration of both local effects and emissions from the Greater London region. The emissions inventory data for the study are described by a 1=1 km grid covering the Greater London Area which measures 60 km east to west and 45 km north to south. Predicted concentrations for a summer and winter period have been calculated and modelled and measured times series data have been compared. Statistical analyses have been carried out to assist in the comparison of model predictions with monitored data. Although no absolute significance can be attached to the numerical values of these measures, taken cumulatively, some conclusions regarding the emissions inventory data and the model performance can be made.