IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i9p3172-d353357.html
   My bibliography  Save this article

The Functional Spatio-Temporal Statistical Model with Application to O 3 Pollution in Beijing, China

Author

Listed:
  • Yaqiong Wang

    (Guanghua School of Management, Peking University, Beijing 100871, China
    These authors contributed equally to this work.)

  • Ke Xu

    (School of Statistics, University of International Business and Economics, Beijing 100029, China
    These authors contributed equally to this work.)

  • Shaomin Li

    (Guanghua School of Management, Peking University, Beijing 100871, China)

Abstract

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( O 3 ) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O 3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O 3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.

Suggested Citation

  • Yaqiong Wang & Ke Xu & Shaomin Li, 2020. "The Functional Spatio-Temporal Statistical Model with Application to O 3 Pollution in Beijing, China," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3172-:d:353357
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/9/3172/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/9/3172/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinhuang Lin & An Zhang & Wenhui Chen & Mingshui Lin, 2018. "Estimates of Daily PM 2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    2. Huang, Hsin-Cheng & Cressie, Noel, 1996. "Spatio-temporal prediction of snow water equivalent using the Kalman filter," Computational Statistics & Data Analysis, Elsevier, vol. 22(2), pages 159-175, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    2. Lara Fontanella & Luigi Ippoliti, 2003. "Dynamic models for space-time prediction via Karhunen-Loève expansion," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 61-78, February.
    3. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    4. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    5. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    6. José Angulo & N. Cressie & C. Wikle & P. Soidán & M. Bande & C. Glasbey & John Kent & Ana Militino & Michael Stein, 1998. "Discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 283-285, December.
    7. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
    8. J. Zhu & J. C. Eickhoff & P. Yan, 2005. "Generalized Linear Latent Variable Models for Repeated Measures of Spatially Correlated Multivariate Data," Biometrics, The International Biometric Society, vol. 61(3), pages 674-683, September.
    9. Kim, Yongku & Berliner, L. Mark, 2016. "Change of spatiotemporal scale in dynamic models," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 80-92.
    10. Qingbin Wei & Lianjun Zhang & Wenbiao Duan & Zhen Zhen, 2019. "Global and Geographically and Temporally Weighted Regression Models for Modeling PM 2.5 in Heilongjiang, China from 2015 to 2018," IJERPH, MDPI, vol. 16(24), pages 1-20, December.
    11. Mingshui Lin & Juan Lin & Caibin Lin & An Zhang & Kaiyong Wang, 2018. "Spatial Diffusion of Taiwanese Enterprises in Mainland China under the Vision of Rural Industrial Vitalization," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    12. Esmail Yarali & Firoozeh Rivaz, 2020. "Incorporating covariate information in the covariance structure of misaligned spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    13. Huang, H.-C. & Martinez, F. & Mateu, J. & Montes, F., 2007. "Model comparison and selection for stationary space-time models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4577-4596, May.
    14. Ting Fung Ma & Fangfang Wang & Jun Zhu & Anthony R. Ives & Katarzyna E. Lewińska, 2023. "Scalable Semiparametric Spatio-temporal Regression for Large Data Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 279-298, June.
    15. Rosaura Fernández-Pascual & María Ruiz-Medina & Jose Angulo, 2004. "Wavelet-based functional reconstruction and extrapolation of fractional random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 417-444, December.
    16. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3172-:d:353357. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.