A1060
Title: Control charts for dynamic process monitoring with an application to air pollution surveillance
Authors: Peihua Qiu - University of Florida (United States) [presenting]
Abstract: Air pollution is a major global public health risk factor. To tackle problems caused by air pollution, governments have put a huge amount of resources into improving air quality and reducing the impact of air pollution on public health. In this effort, it is extremely important to develop an air pollution surveillance system to constantly monitor the air quality over time and give a prompt signal once the air quality is found to deteriorate so that a timely government intervention can be implemented. To monitor a sequential process, a major statistical tool is the statistical process control (SPC) chart. However, traditional SPC charts are based on the assumption that process observations at different time points are independent and identically distributed. These assumptions are rarely valid in environmental data because seasonality and serial correlation are common in such data. To overcome this difficulty, a new control chart is suggested that can properly accommodate dynamic temporal patterns and serial correlation in a sequential process. Thus, it can be used for effective air pollution surveillance.
