Today, the advancement of technology and industrial development, together with other achievements, have brought many harmful effects to human life, one of which is air pollution. The crisis of air pollution in the metropolis of Iran has become one of the main environmental challenges. Therefore, in order to reduce the air pollution of metropolis of Isfahan, management decisions are necessary. Air pollution modeling can play an effective role in the development of air pollution control and management programs. One of these methods is the use of Bayesian networks. Bayesian Belief Networks () is a kind of graphical model that incorporates probabilistic relationships among variables and integrate different sources of knowledge. In this study, the effective variables and management measures were identified on air pollution in Isfahan city and experts' point of view was collected in the form of a questionnaire. Then an influence diagram was developed construct a cause and effect relationships between the variables affecting air pollution and air pollution management.Then, by completing the conditional probability tables based on expert opinions, these charts turned into the Bayesian model,The overall structure of the networks was reviewed by the relevant experts. Quantifying the variables relationships was performed in Netica Software and the behavior of the models was evaluated using sensitivity analysis. The results of air pollution model showed that air pollution in Isfahan was 14.7% in the Keywords : Air pollution, Bayesian Networks, Management,Netica, Isfahan City
