Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

Bibliographic Data

ID15489755
AuthorsSwatantra R Kethireddy (0000-0003-0689-3551, Jackson State University), Swatantra 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 (0000-0002-3407-6674, Jackson State University, corresponding author), Hafiz Ahmad (0000-0002-9366-3502, Department of Biology, Jackson State University, 1400 JR Lynch Street, Jackson, MS 39217, USA), Anjaneyulu Yerramilli (Jackson State University), John Young (0000-0003-4085-9306, 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 (0000-0002-3547-1852, Jackson State University)
Year2014
Volume11
Issue1
Pages983-1000
Publication date2014-01-10
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph110100983
PMID24434594
OpenAlexW2082751620
LanguageEN
Citations received4
References cited15

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

Air pollution · Geography · Geostatistics · Kriging · Meteorology · Multivariate interpolation · Ozone · Physical geography · Spatial variability · Statistics · Tropospheric ozone · Air Quality and Health Impacts · Atmospheric and Environmental Gas Dynamics · Atmospheric chemistry and aerosols · Environmental Science · Mathematics · Pollution