For time-series forecasting problems, there have been several prediction models to data, but the development of a more accurate model is very difficult because of high non-linear and non-stable relations between input and output data. Almost all the models at hand are not applicable online, although online prediction, especially for air quality parameters forecasting, has very important significance for realworld applications. A support vector machine (SVM), as a novel and powerful machine learning tool, can be used for time-series prediction and has been reported to perform well by some promising results. This paper develops an online SVM model to predict air pollutant levels in an advancing time-series based on the monitored air pollutant database in Hong Kong downtown area. The experimental comparison between the online SVM model and the conventional SVM model (non-online SVM model) demonstrates the effectiveness and efficiency in predicting air quality parameters with different time series.