According to the United States Environmental Protection Agency, radon is the number one cause of lung cancer among non-smokers, and it is responsible for about 21,000 lung cancer deaths every year in the United States. In the State of Ohio, 14% of lung cancer deaths are caused due to radon. It is essential to have the radon concentration data for every location (i.e., zip codes) so that necessary preventive measures can be taken up. Measuring the radon concentration across the entire State of Ohio will be very expensive and time consuming. This research focuses on the application of six geographical information system (GIS) based interpolation techniques to estimate the radon concentration in the unmeasured zip codes in the State of Ohio. The radon concentration in homes has been obtained by The University of Toledo researchers from various commercial testing services, university researchers, and county health departments. The data are divided into two sets. The first set uses 80% of the data for training different interpolation schemes, and the second data set includes 20% of the data to evaluate the interpolation techniques. Statistical performance measures such as coefficient of correlation (r), Spearman correlation coefficient (¿¿), slope of the regression line (m), ratio of the intercept of the regression line to the average observed concentrations (b/Co), fractional variance (FV), fraction of prediction within a factor of two of the observations (FA2), model comparison measure (MCM2), geometric mean bias (MG), geometric mean variance (VG), normalized mean square error (NMSE), fractional bias (FB), revised index of agreement (IOAr), accuracy for paired peak (Ap), maximum ratio (Rmax), scatter plots, quantile – quantile (QQ) plots and bootstrap 95% confidence interval estimates based on extreme-end concentrations (i.e., peak-end/low-end), and the mid-range concentrations of indoor air quality (IAQ) models are performed on the predicted data points to evaluate the best interpolation technique.
Considering the statistical indicators for peak-end, low-end and mid-range estimates, it has been found that cokriging is a suitable technique for peak-end estimates, and the radial basis function (RBF) technique meets all the acceptable criteria for low-end and mid-range estimates. After considering the closeness of the greater number of measures to their respective ideal values, graphical representations of the scatter plots and QQ plots, the RBF technique surpasses the other six interpolation techniques. Again, the summary of the bootstrap confidence interval estimates among the techniques indicate that the RBF technique is not significantly different from the other five interpolation techniques under all situations. Therefore, the RBF technique may not be the best technique always when applied to similar sets of dataset from other states and countries. The RBF technique is tentatively suggested in this thesis to perform the interpolation of radon concentration for the unmeasured zip codes in the State of Ohio. This technique is used to understand the extent of radon problems in Ohio. This approach provides a complete picture of radon distribution in the state. It has been found from the zip code based analysis that the number of zip codes exceeding 2.7 pCi/l (World Health Organization (WHO) recommended limit), 4 pCi/l (US Environmental Protection Agency (EPA) action limit), 8 pCi/l and 20 pCi/l are 1300, 693, 28, and 2, respectively after prediction using the RBF technique.