Measurements tell us what the concentrations are (or have been) at a particular location. They cannot tell us what the concentration is going to be in the future, or what it is now at locations where no measurements are being made. 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. We are a very long way from this ideal. Also, it should be remembered that a model is no use unless it has been validated to show that it works, so that the process of model development goes hand in hand with developments in measurement. There are many reasons for using models, such as working out which sources are responsible for what proportion of concentration at any receptor; estimating population exposure on a higher spatial or temporal resolution than is practicable by measurement; targeting emission reductions on the highest contributors; predicting concentration changes over time.