Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

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 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.

Multidrug-resistant tuberculosis (MDR-TB) disease burden in China: a systematic review and spatio-temporal analysis

“…Kriging spatial interpolation analysis is a classical geostatistical analysis method and based on rules of space at correlation quantize between the sample points, which we can calculate the proportion of incident TB cases with MDR for no data area by using the known sample points area [14–16], the cross validation method can improve the accuracy of prediction. There are scarce data available in the western area, therefore, the spatial interpolation analysis was conducted only in eastern and central area, and used the provincial data in the western areas to estimate the MDR-TB for nationwide.…”

Diseases Burden of Chronic Obstructive Pulmonary Disease (COPD) Attributable to Ground-Level Ozone in Thailand: Estimates Based on Surface Monitoring Measurements Data

“…In addition to the IDW method, several studies used the Kriging method to evaluate O 3 exposure ( Denby et al, 2010 ; Gorai, Tuluri, & Tchounwou, 2014 ; Kethireddy, Tchounwou, Ahmad, Yerramilli, & Young, 2014 ; Roberts, Voss, & Knight, 2014 ). Kriging weights the distance between measured locations based on its spatial correlation to produce variograms and a covariance factor for predicting the unknown locations ( Wong, Yuan, & Perlin, 2004 ).…”

Study on Air Pollution and Control Investment from the Perspective of the Environmental Theory Model: A Case Study in China, 2005–2014

“…The Kriging interpolation is also called the spatial local interpolation method. It can implement a linear optimal unbiased estimation of the data at unknown sampling points in a region [40]. IDW is more suitable for the proliferation of point source data, while Kriging is more suitable for continuous data diffusion.…”