Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO2), respirable suspended particulates (RSP) and nitrogen dioxide (NO2), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region.