Air pollution emerges as an imminent issue in metropolitan cities like Hong Kong, and attracts much attention in recent years. Prediction of pollutant levels and their tendency is an important topic in environmental science today. To achieve such prediction tasks, the use of neural network (NN), in particular, the multi-layer perceptron, is regarded as a cost-effective technique superior to traditional statistical methods. But the training of the multi-layer perceptron, normally featured with back-propagation (BP) algorithm or other gradient algorithms, still faces certain drawbacks, e.g., very slow convergence, easily getting stuck in a local minimum, etc. In this paper, a newly developed method, particle swarm optimization (PSO) model, is adopted to train the perceptron and to predict the pollutant levels. As a result, a new neural network model, PSO-based approach, is established and completed. The approach is proved to be feasible and effective by applying to some real air-quality problems and by comparing with the simple BP algorithm.