Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong.

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of air pollutant parameters becomes an important and popular topic in environmental science. Airborne pollution is a serious, and will be a major problem in Hong Kong within the next few years. In Hong Kong, Respirable Suspended Particulate (RSP) and Nitrogen Oxides NOx and NO2 are major air pollutants due to the dominant diesel fuel usage by public transportation and heavy vehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are of significance to public and the city image. The multi-layer perceptron (MLP) neural network is regarded as a reliable and cost-effective method to achieve such tasks. The works presented here involve developing an improved neural network model, which combines the principal component analysis (PCA) technique and the radial basis function (RBF) network, and forecasting the pollutant levels and tendencies based in the recorded data. In the study, the PCA is firstly used to reduce and orthogonalize the original input variables (data), these treated variables are then used as new input vectors in RBF neural network model established for forecasting the pollutant tendencies. Comparing with the general neural network models, the proposed model possesses simpler network architecture, faster training speed, and more satisfactory predicting performance. This improved model is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP. NOx and NO2 concentrations with the actual data of these pollutants recorded at the monitory station, the effectiveness of the proposed model has been proven. Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and has advantages over the traditional neural network methods.