Study On Air Quality Medium-term And Long-term Forecasting Method

Air is the essential element for human being and air quality influents directly the life condition and health level of people. As the main branch of the environmental problems, air pollution is being paid more and more attention by countries of the world. There is no time to delay for air pollution control. Air quality forecasting is the precondition for the environmental administration to make decisions and bring forward pollution prevention and cure measures effectively. Currently, in the field of the air pollution short term forecasting, the correction models of Gaussian distribution are widely used, which vary according to the specific geographic environment and weather factor. Such kinds of models are complicated because there are too many parameters being adopted. However, estimation of the parameters includes certain fuzzy factors itself, which inevitably results in an unsatisfactory prediction. Therefore, based on the principle of statistics and taking the fall dust monitoring data of each season monthly average from year 1986 to 2000 in some city of North China as a study case, medium- term and long-term forecasting methods to air quality were studied systematically, which aimed at finding simplified models and combined forecasting models. That is, finding the models which can both reach the required precision and avoid adopting too many parameters. The main air quality forecasting models discussed in this paper were as follows:Time series analysis modelGrey modelArtificial neural network modelOptimal combination modelGM-AR model (combination of Grey model and Auto-regression model in time series analysis)GM-ANN model (combination of Grey system model and Artificial Neural Network model) AR-ANN model (combination of Auto-regression model and Artificial Neural Network model)Wavelet neural network modelThrough corresponding computer programs, longitudinal as well as transverse comparisons to the forecasting effect between the models were made. Then, with a study case, this paper concluded that, under the situation of lacking environmental information, as long as suitable model was chosen by applying statistics to analyze historical monitoring data, it could satisfy the engineering requirement to medium-term and long-term air quality forecasting.