Synoptic typing and its application to the assessment of climatic impact on concentrations of sulfur dioxide and nitrogen oxides in Hong Kong

Abstract

Principal components analysis and average linkage clustering procedure are used to automatically classify seasonal distinctive synoptic categories based on 6-hourly surface meteorological data for Hong Kong. This procedure is able to identify the specific categories exhibiting particularly high mean pollution concentrations and producing high frequency of severe pollution events. It is found that high SO 2 and NO x concentrations are usually associated with the certain spring and summer air masses with moderate to strong southwesterly or westerly winds as well as related to the autumn and winter synoptic categories with light northerly or easterly winds. This procedure has a potential to forecast air pollution concentrations, which could be achieved through predicting the arrival of the most polluted synoptic categories.