Online monitoring of air quality using PCA-based sequential learning

Air pollution surveillance is critically important for public health. One air pollutant, ozone, is extremely challenging to analyze properly, as it is a secondary pollutant caused by complex chemical reactions in the air, and does not emit directly into the atmosphere. Numerous environmental studies confirm that ozone concentration levels are associated with meteorological conditions, and long-term exposure to high ozone concentration levels is associated with the incidence of many diseases, including asthma, respiratory, and cardiovascular diseases. Thus, it is important to develop an air pollution surveillance system to collect both air pollution and meteorological data and monitor the data continuously over time. To this end, statistical process control (SPC) charts provide a major statistical tool. But, most existing SPC charts are designed for cases when the in-control (IC) process observations at different times are assumed to be independent and identically distributed. The air pollution and meteorological data would not satisfy these conditions due to serial data correlation, high dimensionality, seasonality, and other complex data structure. Motivated by an application to monitor the ground ozone concentration levels in the Houston-Galveston-Brazoria (HGB) area, we developed a new process monitoring method using principal component analysis and sequential learning. The new method can accommodate high dimensionality, time-varying IC process distribution, serial data correlation, and non-parametric data distribution. It is shown to be a reliable analytic tool for on-line monitoring of air quality.