Influence and Evaluation of Meteorological Parameters on Urban Air Quality Modelling Using CALINE4

In recent years, concern about urban air quality has been directed towards traffic related pollution, and there is increasing interest in identifying the means through which these can be reduced. Such quantification can be achieved using either of two approaches. The more comprehensive would consist of large-scale atmospheric monitoring, carried out at a wide range of sites and maintained for a long duration at each. This approach would involve significant expense, both initial and recurrent. The second option is to develop a computer-based model of the problem and conduct a more limited programme of monitoring specifically targeted at providing results, which allow the developed model to be accurately calibrated and verified. Reliable and accurate input data is an essential requirement of any mathematical model. In the case of a dispersion model e.g. CALINE4, a primary input is processed meteorological data, along topography and an emissions inventory. Local meteorological data is impossible to obtain for all areas. The nearest synoptic station may be tens-of-kilometres away from the study area. This does not cause problems for long-term annual averages but short-term hourly averages can differ substantially. In addition, pre-processing of meteorological data for dispersion models can result in large differences in estimated concentrations. For example, two meteorological data sets are available in Ireland (i.e. MET EIREANN and Trinity Consultants, USA). However, a comparison reveals substantial differences between both data sets due to the pre-processing methods. The scientific novelty of this research is to investigate the effect of employing different meteorological data on estimating pollution concentrations. Meteorological data was collected locally and at a nearby synoptic station for one year and compared. Estimated pollutant concentrations were compared to simultaneous measurements.