U.S. EPA 1995a 1995b 2005 Liao et al. 2004 2005a Sullivan et al. 2005 Wellenius et al. 2005 Whitsel et al. 2004 a b c d Materials and Methods 10 2.5 U.S. EPA 2005 10 2.5 Liao et al. 2005b Whitsel et al. 2004 2005 10 2.5 Whitsel 2006 Cressie 1993a Davis 2002 Gribov et al. 2004 Jian et al. 1996 a b c ESRI Inc. 2001 Liao et al. 2005c Gribov et al. 2004 Jian et al. 1996 Cressie 1993b Johnston 2001 Results 10 2.5 10 2.5 10 2.5 10 2.5 10 2.5 10 2.5 3 3 10 2.5 10 Figure 1 Comparisons of three widely used spatial models Tables 1 2 10 2.5 3 10 3 2.5 Comparisons of default and manually adjusted semivariograms Table 3 10 Comparisons of regular versus lognormal ordinary krigings 10 n 10 10 Table 4 10 10 Table 4 Table 5 10 10 10 10 3 3 10 10 2.5 Comparisons between national and regional krigings 10 Figure 1 Table 6 Discussion Krige 1966 Legendre and Fortin 1989 U.S. EPA 1995a 1995b 2005 Abbey et al. 1991 1999 Dockery et al. 1993 Katsouyanni et al. 1996 2001 Miller et al. 2004 2005 Pope et al. 2004 Samet et al. 2000a 2000b Moore and Carpenter 1999 Zimmerman 1999 Whitsel 2006 WHI Study Group 1998 10 2.5 Webster and Oliver 2001 10 2.5 Tables 1 2 a b 10 Table 3 10 Cressie 1993b Johnston 2001 10 10 Considering the number of study participants and the length of study period (1994–2003) for the Environmental Epidemiology of Arrhythmogenesis in WHI study, development of an automated procedure enabling large-scale daily krigings and semivariogram cross-validations was critical. In this study, we decided to use ArcView for predicting individuals’ PM exposure concentrations because of the flexibility it offers for automation. Because ArcView GIS relies on either the weighted least-squares method or visual adjustment to create semivariograms, we did not compare the relative performance of semivariograms generated using alternative methods such as maximum likelihood and restricted maximum likelihood. For generating semivariograms, we compared only three popular spatial models (spherical, exponential, and Gaussian). Our results, however, do not invalidate alternative spatial models (e.g., power). In the end, we selected the spherical model for our study because it is the most studied model, and its assumption pertaining to the spatial correlation of data is probably closest to our pollutant data. Furthermore, the spherical model seemed to perform as well as or slightly better than the remaining models in terms of cross-validation parameters. Denby et al. (2005) Although the primary objective of our study is to assess the short-term relationship between PM and cardiac responses, the proposed kriging method also enables us to calculate the long-term cumulative exposure of an individual by taking into account the change of his or her residences over time, because the WHI study recorded the residential location history over 10 years. Nevertheless, from the environmental perspective, an inherited limitation of the kriging-based approach is that the estimations of the PM concentrations will provide only surrogates, or the best guesses, of the true exposure levels at the locations of interest. Thus, the accuracy of the estimations depends highly on the quality of the measured data and their spatial correlation. Even if the estimations were made with a high level of confidence, they cannot be directly interpreted as the true individual-level exposures. However, to correlate individual level cardiac responses with a surrogate of location-specific exposure, our approach represents one of the best available methods for a large-scale population-based study. In summary, our investigation of GIS approaches for estimating daily mean geocoded location-specific air pollutant concentrations and their SEs supports the use of a spherical model to perform lognormal ordinary kriging on a national scale. Our findings also support the use of default-generated semivariograms (estimated using the weighted least-squares method) without visual adjustment. We developed a semiautomated program to access and execute ArcView to implement these approaches for large-scale daily kriging estimations and semivariogram cross-validations. Detailed information about this program can be obtained on request.