P. Goovaerts *, G. Avruskin *, J. Meliker **, M. Slotnick **, G. Jacquez *, J. Nriagu **
* Biomedware, Inc.
516 North State Street
Ann Arbor, MI 48104, USA
Ph.  (734) 913-1098; Fax: (734) 913-2201
E-mail: goovaerts@biomedware.com 
** School of Public Health
The University of Michigan
Ann Arbor, MI 48109-2029, USA

Abstract
The last decade has witnessed an increasing interest in assessing health risks caused by exposure to contaminants present in the soil, air, and water. A key component of any exposure study is a reliable model for the space-time distribution of pollutants. This paper compares the performances of multiGaussian and indicator kriging for modeling probabilistically the space-time distribution of  arsenic concentrations in groundwater of Southeast Michigan, accounting for information collected at private residential wells and the hydrogeochemistry of the area. This model will later be combined with a space-time information system to compute individual exposures and analyze its relationship to the occurrence of bladder cancer.  Because of the small  changes in concentration observed in time, the study has focused on the spatial variability of arsenic, which can be considerable over very short distances. Factorial kriging was used to filter this short-range variability, leading to a significant increase (17 to 65%) in the proportion of variance explained by secondary information, such as type of surficial rock formation and proximity to Marshall  Sandstone subcrop. Cross validation of 8,212 well data shows that accounting for this regional background does not improve the local prediction of arsenic, indicating the presence of unexplained sources of variability and the importance to model the uncertainty attached to these predictions.  

Keywords: indicator kriging, arsenic, cross validation, exposure

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.