Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

Swatantra R. Kethireddy, Paul B. Tchounwou, Hafiz A. Ahmad, Anjaneyulu Yerramilli and John H. Young
Additional contact information
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
Hafiz A. Ahmad: Department of Biology, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217, USA
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
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

IJERPH, 2014, vol. 11, issue 1, 1-18

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.

Keywords: tropospheric ozone (O3); geostatistical analysis; prediction; interpolation; spatial resolution; visualization; Geographical Information Systems (GIS) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/11/1/983/pdf (application/pdf)
https://www.mdpi.com/1660-4601/11/1/983/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:11:y:2014:i:1:p:983-1000:d:32052

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().