IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i11p3892-3906.html
   My bibliography  Save this article

Spatial prediction of ozone concentration profiles

Author

Listed:
  • Temiyasathit, Chivalai
  • Kim, Seoung Bum
  • Park, Sun-Kyoung

Abstract

Ground level ozone is one of the major air pollutants in many urban areas. Ozone formation affects ecosystems and is known to be associated with many adverse health issues in humans. Effective modeling of ozone is a necessary step to develop a system to warn residents of high ozone levels. In the present study we propose a statistical procedure that uses multiscale and functional data analysis to improve the spatial prediction of ozone concentration profiles in the Dallas Fort Worth (DFW) area of Texas. This study uses daily eight-hour ozone concentrations and meteorological predictors during a period between 2003 and 2006 at 14 monitoring sites in the DFW area. Wavelet transformation was used as a means of multiscale data analysis, followed by functional modeling to reduce model complexity. Kriging was then used for spatial prediction. The experimental results with real data demonstrated that the proposed procedures achieved acceptable accuracy of spatial prediction.

Suggested Citation

  • Temiyasathit, Chivalai & Kim, Seoung Bum & Park, Sun-Kyoung, 2009. "Spatial prediction of ozone concentration profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3892-3906, September.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:11:p:3892-3906
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00134-0
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gabriel Huerta & Bruno Sansó & Jonathan R. Stroud, 2004. "A spatiotemporal model for Mexico City ozone levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 231-248, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    2. Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.

    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. Marcelo Cunha & Dani Gamerman & Montserrat Fuentes & Marina Paez, 2017. "A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 919-939, November.
    2. Stefano F. Tonellato, 2005. "Identifiability Conditions for Spatio-Temporal Bayesian Dynamic Linear Models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 81-101.
    3. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    4. Sujit K. Sahu & Alan E. Gelfand & David M. Holland, 2010. "Fusing point and areal level space–time data with application to wet deposition," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 77-103, January.
    5. Andrew Hoegh & Scotland Leman, 2015. "A spatio-temporal model for assessing winter damage risk to east coast vineyards," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 834-845, April.
    6. Jonas Wallin & David Bolin, 2015. "Geostatistical Modelling Using Non-Gaussian Matérn Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 872-890, September.
    7. GEMOETS, DARREN Ethan, 2022. "Hierarchical Bayesian Approach to Fitting Discretized Partial Differential Equation Models to Spatial-temporal Data: The Effects of Numerical Instability on Parameter Estimates," OSF Preprints srg2t, Center for Open Science.
    8. Meraz, M. & Alvarez-Ramirez, J. & Echeverria, J.C., 2017. "Asymmetric correlations in the ozone concentration dynamics of the Mexico City Metropolitan Area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 377-386.
    9. Kyu Jong Lee & Hyungu Kahng & Seoung Bum Kim & Sun Kyoung Park, 2018. "Improving Environmental Sustainability by Characterizing Spatial and Temporal Concentrations of Ozone," Sustainability, MDPI, vol. 10(12), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    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:eee:csdana:v:53:y:2009:i:11:p:3892-3906. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.