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Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

Author

Listed:
  • Swatantra R. Kethireddy

    (Trent Lott Geospatial and Visualization Research Center, College of Science Engineering and Technology, Jackson State University, Mississippi E-Center, 1230 Raymond Rd, Jackson, MS 39204, USA)

  • Paul B. Tchounwou

    (NIH RCMI Center for Environmental Health, Jackson State University, 1400 JR Lynch Street, P.O. Box 18750, Jackson, MS 39217, USA
    These authors contributed equally to this work.)

  • Hafiz A. Ahmad

    (Department of Biology, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217, USA
    These authors contributed equally to this work.)

  • Anjaneyulu Yerramilli

    (Trent Lott Geospatial and Visualization Research Center, College of Science Engineering and Technology, Jackson State University, Mississippi E-Center, 1230 Raymond Rd, Jackson, MS 39204, USA
    These authors contributed equally to this work.)

  • John H. Young

    (Trent Lott Geospatial and Visualization Research Center, College of Science Engineering and Technology, Jackson State University, Mississippi E-Center, 1230 Raymond Rd, Jackson, MS 39204, USA
    These authors contributed equally to this work.)

Abstract

Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O 3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O 3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O 3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O 3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O 3 pollution map data can be further used in analysis and modeling studies. Kriging results and O 3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O 3 -related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

Suggested Citation

  • Swatantra R. Kethireddy & Paul B. Tchounwou & Hafiz A. Ahmad & Anjaneyulu Yerramilli & John H. Young, 2014. "Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics," IJERPH, MDPI, vol. 11(1), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:1:p:983-1000:d:32052
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    References listed on IDEAS

    as
    1. Samuel D. Brody & B. Mitchell Peck & Wesley E. Highfield, 2004. "Examining Localized Patterns of Air Quality Perception in Texas: A Spatial and Statistical Analysis," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1561-1574, December.
    2. Anjaneyulu Yerramilli & Venkata B. Dodla & Srinivas Desamsetti & Srinivas V. Challa & John H. Young & Chuck Patrick & Julius M. Baham & Robert L. Hughes & Sudha Yerramilli & Francis Tuluri & Mark G. H, 2011. "Air Quality Modeling for the Urban Jackson, Mississippi Region Using a High Resolution WRF/Chem Model," IJERPH, MDPI, vol. 8(6), pages 1-21, June.
    3. O. R. Cooper & D. D. Parrish & A. Stohl & M. Trainer & P. Nédélec & V. Thouret & J. P. Cammas & S. J. Oltmans & B. J. Johnson & D. Tarasick & T. Leblanc & I. S. McDermid & D. Jaffe & R. Gao & J. Stith, 2010. "Increasing springtime ozone mixing ratios in the free troposphere over western North America," Nature, Nature, vol. 463(7279), pages 344-348, January.
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    Cited by:

    1. Zhenyi Jia & Shenglu Zhou & Quanlong Su & Haomin Yi & Junxiao Wang, 2017. "Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network," IJERPH, MDPI, vol. 15(1), pages 1-14, December.
    2. Mohammad Maleki & Kevin Soria, 2020. "Multivariate Geostatistical Modeling and Risk Analysis of Beach Litter: A Case Study of Playa Blanca Beach, Chile," IJERPH, MDPI, vol. 17(22), pages 1-15, November.

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