TAPM: a practical approach to prognostic meteorological and air pollution modelling

Air pollution predictions for environmental impact assessments are usually generated by Gaussian plume/puff models driven by observationally-based meteorological inputs. An alternative approach is to use prognostic meteorological and air pollution models, which are better founded than the Gaussian approach, but tend to require much expertise and time to set up and large computing resources to run. This paper provides an overview of the science and verification of The Air Pollution Model (TAPM), which was designed to apply the best science in an easy-to-use and fast-to-run model. TAPM is a PC-based, nestable, prognostic meteorological and air pollution model (with photochemistry) driven by a Graphical User Interface, and is a viable tool for year-long simulations. Datasets of the important inputs needed for meteorological simulations (such as terrain and land-use data and synoptic analyses) accompany the model, allowing quick GUI configuration of the model for any region. We present results from the application of TAPM for urban and coastal areas in Australia, for two United States tracer experiments (Kincaid and Indianapolis) used for international model inter-comparison, and for point source dispersion in wind-tunnel building wakes. The meteorological results show that TAPM performs well in coastal, inland, and complex terrain, in sub-tropical to mid-latitude conditions, for both case studies and year-long simulations. The pollution results show that TAPM performs well for a range of important phenomena such as nocturnal inversion break-up fumigation, convective dispersion, shoreline fumigation, building wakes, and general dispersion in complex rural and urban conditions. In particular, the TAPM performance is very good for the prediction of extreme pollution statistics, important for environmental impact assessments, for both non-reactive (tracer) and reactive (nitrogen dioxide, ozone and particulate) pollutants for a variety of sources (e.g. industrial stacks and surface or urban emissions). (c) 2004 Elsevier Ltd. All rights reserved.