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Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks

Author

Listed:
  • Xiangyu Zhao

    (College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
    Institute of Zhejiang University-Quzhou, Quzhou 324000, China)

  • Kuang Cheng

    (College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
    Institute of Zhejiang University-Quzhou, Quzhou 324000, China)

  • Wang Zhou

    (College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
    Institute of Zhejiang University-Quzhou, Quzhou 324000, China)

  • Yi Cao

    (College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
    Institute of Zhejiang University-Quzhou, Quzhou 324000, China)

  • Shuang-Hua Yang

    (College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
    Institute of Zhejiang University-Quzhou, Quzhou 324000, China)

Abstract

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T 2 and Q , which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

Suggested Citation

  • Xiangyu Zhao & Kuang Cheng & Wang Zhou & Yi Cao & Shuang-Hua Yang, 2022. "Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks," IJERPH, MDPI, vol. 19(12), pages 1-21, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7201-:d:837012
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    References listed on IDEAS

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    1. Zhengqiu Zhu & Bin Chen & Sihang Qiu & Rongxiao Wang & Feiran Chen & Yiping Wang & Xiaogang Qiu, 2018. "An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries," IJERPH, MDPI, vol. 15(4), pages 1-20, March.
    2. Jie Zeng & Guilin Han & Qixin Wu & Yang Tang, 2019. "Heavy Metals in Suspended Particulate Matter of the Zhujiang River, Southwest China: Contents, Sources, and Health Risks," IJERPH, MDPI, vol. 16(10), pages 1-16, May.
    3. Tavoos Hassan Bhat & Guo Jiawen & Hooman Farzaneh, 2021. "Air Pollution Health Risk Assessment (AP-HRA), Principles and Applications," IJERPH, MDPI, vol. 18(4), pages 1-22, February.
    4. Zhengqiu Zhu & Bin Chen & Genserik Reniers & Laobing Zhang & Sihang Qiu & Xiaogang Qiu, 2017. "Playing Chemical Plant Environmental Protection Games with Historical Monitoring Data," IJERPH, MDPI, vol. 14(10), pages 1-23, September.
    5. Chaofeng Shao & Juan Yang & Xiaogang Tian & Meiting Ju & Lei Huang, 2013. "Integrated Environmental Risk Assessment and Whole-Process Management System in Chemical Industry Parks," IJERPH, MDPI, vol. 10(4), pages 1-22, April.
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