  This paper describes an agent based approach for simulating the control of an
air pollution crisis. A Gaussian Plum air pollution dispersion model (GPD) is
combined with an Artificial Neural Network (ANN) to predict the concentration
levels of three different air pollutants. The two models (GPM and ANN) are
integrated with a MAS (multi-agent system). The MAS models pollutant sources
controllers and air pollution monitoring agencies as software agents. The
population of agents cooperates with each other in order to reduce their
emissions and control the air pollution. Leaks or natural sources of pollution
are modelled as uncontrolled sources. A cooperation strategy is simulated and
its impact on air pollution evolution is assessed and compared. The simulation
scenario is built using data about Annaba (a city in NorthEast Algeria). The
simulation helps to compare and assess the efficiency of policies to control
air pollution during crises, and takes in to account uncontrolled sources.
