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A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series

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  • Shen, Chenhua

Abstract

We applied traditional principal component analysis (TPCA) and nonstationary principal component analysis (NSPCA) to determine principal components in the six daily air-pollutant concentration series (SO2, NO2, CO, O3, PM2.5 and PM10) in Nanjing from January 2013 to March 2016. The results show that using TPCA, two principal components can reflect the variance of these series: primary pollutants (SO2, NO2, CO, PM2.5 and PM10) and secondary pollutants (e.g., O3). However, using NSPCA, three principal components can be determined to reflect the detrended variance of these series: 1) a mixture of primary and secondary pollutants, 2) primary pollutants and 3) secondary pollutants. Various approaches can obtain different principal components. This phenomenon is closely related to methods for calculating the cross-correlation between each of the air pollutants. NSPCA is a more applicable, reliable method for analyzing the principal components of a series in the presence of nonstationarity and for a long-range correlation than can TPCA. Moreover, using detrended cross-correlation analysis (DCCA), the cross-correlation between O3 and NO2 is negative at a short timescale and positive at a long timescale. In hourly timescales, O3 is negatively correlated with NO2 due to a photochemical interaction, and in daily timescales, O3 is positively correlated with NO2 because of the decomposition of O3. In monthly timescales, the cross-correlation between O3 with NO2 has similar performance to those of O3 with meteorological elements. DCCA is again shown to be more appropriate for disclosing the cross-correlation between series in the presence of nonstationarity than is Pearson’s method. DCCA can improve our understanding of their interactional mechanisms.

Suggested Citation

  • Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:453-464
    DOI: 10.1016/j.physa.2016.09.014
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    Cited by:

    1. Dong, Xiaofeng & Fan, Qingju & Li, Dan, 2023. "Detrending moving-average cross-correlation based principal component analysis of air pollutant time series," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Anderson Palmeira & Éder Pereira & Paulo Ferreira & Luisa Maria Diele-Viegas & Davidson Martins Moreira, 2022. "Long-Term Correlations and Cross-Correlations in Meteorological Variables and Air Pollution in a Coastal Urban Region," Sustainability, MDPI, vol. 14(21), pages 1-12, November.
    3. He, Yongxiu & Pang, Yuexia & Zhang, Qi & Jiao, Zhe & Chen, Qian, 2018. "Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory," Renewable Energy, Elsevier, vol. 122(C), pages 643-653.

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