March 2024 Online monitoring of air quality using PCA-based sequential learning
Xiulin Xie, Nicole Qian, Peihua Qiu
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Ann. Appl. Stat. 18(1): 600-625 (March 2024). DOI: 10.1214/23-AOAS1803

Abstract

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 nonparametric data distribution. It is shown to be a reliable analytic tool for online monitoring of air quality.

Funding Statement

This research is supported in part by an NSF grant.

Acknowledgments

The authors thank the Editor, the Associate Editor, and three referees for their constructive comments and suggestions, which improved the quality of the paper greatly.

Citation

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Xiulin Xie. Nicole Qian. Peihua Qiu. "Online monitoring of air quality using PCA-based sequential learning." Ann. Appl. Stat. 18 (1) 600 - 625, March 2024. https://doi.org/10.1214/23-AOAS1803

Information

Received: 1 September 2022; Revised: 1 July 2023; Published: March 2024
First available in Project Euclid: 31 January 2024

Digital Object Identifier: 10.1214/23-AOAS1803

Keywords: Air pollution surveillance , dynamic processes , High-dimensional data , Principal Component Analysis , seasonality , self-starting charts

Rights: Copyright © 2024 Institute of Mathematical Statistics

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Vol.18 • No. 1 • March 2024
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