Abstract: Air pollution forecasting provides early warning before air pollution issue occurs,thus protects human health and living environment.A neural network model optimized by genetic algorithm was developed in order to predict PM₁₀ concentrations in Beijing.The genetic algorithm was used to optimize the initial weights and threshold of the BP neural network in simulation.Astringency of network and accuracy of prediction were effectively improved.The improved network and Models-3 Community Multi-scale Air Quality(CMAQ) modeling system were both applied in the prediction of short-term PM₁₀ concentration in autumn 2002 in Beijing.Resultsshowed good prediction capability of both models,and the mean relative errors were separately 0.21 and 0.26.When applied in short-term air pollution forecasting,neural network is of similar prediction accuracy compared with CMAQ.It is an effective alternate method for air pollution forecasting in areas where mathematical model on air pollution can't be widely applied.