Analysis of the distribution of airborne pollution using genetic algorithms

Increasing environmental awareness has been an important factor behind the development of receptor models, which attempt to identify the sources of airborne pollution reaching a monitoring station by analysis of the profile of pollutants collected. Despite the central role of such models in understanding the movement of air pollution, they yield results which contain significant uncertainty. This paper reports the application of the Genetic Algorithm (GA) to this source apportionment problem. Implementation of a matrix formulation for the GA allows it to tackle the many-source/many-receptor problem, with results which suggest the GA approach is a significant advance over current models.