Spatial-temporal Decision Support System for Dynamic Monitoring and Analysis of Air Quality in the South China-Hong Kong

Principal Investigator:
Leung, Yee

Co-investigator(s):
Lam, Kin Che
Fung, Tung
Anh, Vo Van

Summary:
Rapid economic development in South China has brought about abrupt changes in the environment. To better monitor and manage the extent of air pollution, a good decision support system which can efficiently update our knowledge of the changes in the environment must be devised so that effective management schemes can be formulated and implemented.

The purpose of this project is to develop a novel approach which, without the limitations of current techniques, aims at achieving an adequate description of the air pollution situation in the South China-Hong Kong region, and to build a powerful spatial decision support system (SDSS) which integrates the procedures with geographic information system (GIS) for effective analysis, processing, and display of monitoring data for strategic planning.

The approach takes into account the stochastic nature of the atmosphere. It employs the routinely available air quality monitoring data, and can be implemented at relatively low computing costs. In particularly, we aim at studying the following problems:

• Trend analysis and impact of air quality management policy;
• Classification of the South China-Hong Kong airshed;
• A multi-variate causality model for pollution episode prediction; and
• A space-time stochastic model of South China-Hong Kong air pollution.
  

This space-time stochastic model will be a key component of an air quality management system, which can be used to examine the cost and environment consequences of alternative pollution abatement strategies. The prototype SDSS will service as an efficient and effective management tool to combat air pollution problems in the region. The project paves the road for building a real-time forecasting system for the South China-Hong Kong region.