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Spatial and Temporal Variabilities of PM 2.5 Concentrations in China Using Functional Data Analysis

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  • Deqing Wang

    (School of Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, Jiangsu, China)

  • Zhangqi Zhong

    (School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, Zhejiang, China)

  • Kaixu Bai

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China)

  • Lingyun He

    (School of Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, Jiangsu, China)

Abstract

As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM 2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM 2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM 2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM 2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM 2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM 2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM 2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM 2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM 2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally.

Suggested Citation

  • Deqing Wang & Zhangqi Zhong & Kaixu Bai & Lingyun He, 2019. "Spatial and Temporal Variabilities of PM 2.5 Concentrations in China Using Functional Data Analysis," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1620-:d:214738
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    References listed on IDEAS

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    1. Jeng‐Min Chiou & Pai‐Ling Li, 2007. "Functional clustering and identifying substructures of longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 679-699, September.
    2. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    3. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    4. Ramsay, James O. & Ramsey, James B., 2002. "Functional data analysis of the dynamics of the monthly index of nondurable goods production," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 327-344, March.
    5. Ruey S. Tsay, 2016. "Some Methods for Analyzing Big Dependent Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 673-688, October.
    6. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    7. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
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    1. Manuel Oviedo-de La Fuente & Celestino Ordóñez & Javier Roca-Pardiñas, 2020. "Functional Location-Scale Model to Forecast Bivariate Pollution Episodes," Mathematics, MDPI, vol. 8(6), pages 1-12, June.

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