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Open AccessArticle

Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

1
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
2
NIH RCMI Center for Environmental Health, Jackson State University, 1400 JR Lynch Street, P.O. Box 18750, Jackson, MS 39217, USA
3
Department of Biology, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2014, 11(1), 983-1000; https://doi.org/10.3390/ijerph110100983
Received: 14 November 2013 / Revised: 17 December 2013 / Accepted: 19 December 2013 / Published: 10 January 2014
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 O3 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 O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 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. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 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 O3-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. View Full-Text
Keywords: tropospheric ozone (O3); geostatistical analysis; prediction; interpolation; spatial resolution; visualization; Geographical Information Systems (GIS) tropospheric ozone (O3); geostatistical analysis; prediction; interpolation; spatial resolution; visualization; Geographical Information Systems (GIS)
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MDPI and ACS Style

Kethireddy, S.R.; Tchounwou, P.B.; Ahmad, H.A.; Yerramilli, A.; Young, J.H. Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics. Int. J. Environ. Res. Public Health 2014, 11, 983-1000. https://doi.org/10.3390/ijerph110100983

AMA Style

Kethireddy SR, Tchounwou PB, Ahmad HA, Yerramilli A, Young JH. Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics. International Journal of Environmental Research and Public Health. 2014; 11(1):983-1000. https://doi.org/10.3390/ijerph110100983

Chicago/Turabian Style

Kethireddy, Swatantra R.; Tchounwou, Paul B.; Ahmad, Hafiz A.; Yerramilli, Anjaneyulu; Young, John H. 2014. "Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics" Int. J. Environ. Res. Public Health 11, no. 1: 983-1000. https://doi.org/10.3390/ijerph110100983

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