ABSTRACT
Air pollution models help us to understand the way air pollutants behave in the environment. In principle, a perfect model would enable the spatial and temporal variations in pollutant concentration to be predicted to sufficient accuracy for all practical purposes, and would make measurements unnecessary. Dispersion models are based on a detailed understanding of physical, chemical and fluid dynamical processes in the atmosphere. Receptor models are based on the relationships between a data set of measured concentrations at the receptor and a data set of emissions that might affect those concentrations. Stochastic models are based on semi-empirical mathematical relationships between the pollutant concentrations and any factors that might affect them, regardless of the atmospheric physical processes. The main meteorological factors that affect dispersion are wind direction, wind speed and atmospheric turbulence (which is closely linked with the concept of stability). Chemical mass balance was the first receptor modelling method developed for source attribution.
