HR: 0800h
AN: NG41A-0425    [Abstracts]
TI: Geostatistical Modeling of the Spatial Variability of Arsenic in Groundwater of Southeast Michigan
AU: Avruskin, G
EM: avruskin@biomedware.com
AF: Biomedware Inc., 516 North State Street, Ann Arbor, MI 48104 United States
AU: * Goovaerts, P
EM: goovaerts@biomedware.com
AF: Biomedware Inc., 516 North State Street, Ann Arbor, MI 48104 United States
AU: Meliker, J
EM: jmeliker@umich.edu
AF: School of Public Health, The University of Michigan, Ann Arbor, MI 48109-2029 United States
AU: Slotnick, M
EM: slotnick@umich.edu
AF: School of Public Health, The University of Michigan, Ann Arbor, MI 48109-2029 United States
AU: Jacquez, G M
EM: Jacquez@BioMedware.com
AF: Biomedware Inc., 516 North State Street, Ann Arbor, MI 48104 United States
AU: Nriagu, J O
EM: jnriagu@umich.edu
AF: School of Public Health, The University of Michigan, Ann Arbor, MI 48109-2029 United States
AB: 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 assess the risk associated with exposure to low levels of arsenic in drinking water (typically 5-100 $\mu$g/L), in particular for the development of bladder cancer. Because of the small changes in concentration observed in time, the study has focused on the spatial variability of arsenic. This study confirmed results in the literature that reported intense spatial non-homogeneity of As concentration, resulting in samples that greatly vary even when located a few meters apart. Indicator semivariograms further showed a better spatial connectivity of low concentrations while values exceeding 32 $\mu$g/L (10% of wells) are spatially uncorrelated. Secondary information, such as proximity to Marshall Sandstone, helped only the prediction at a regional scale (i.e. beyond 15 kms), leaving the short-range variability largely unexplained. Several geostatistical tools were tailored to the features of the As dataset: (1) semivariogram values were standardized by the lag variance to correct for the preferential sampling of wells with high concentrations, (2) semivariogram modeling was conducted under the constraint of reproduction of the nugget effect inferred from colocated well measurements, (3) kriging systems were modified to account for repeated measurements at a series of wells while avoiding non-invertible kriging matrices, (4) kriging-based smoothing was combined with multivariate regression to predict the regional background of arsenic concentrations across the study area. Cross-validation indicated the little benefit of secondary information in local prediction of arsenic concentrations. Slightly better results were obtained using univariate indicator kriging which generated the smallest mean absolute error of prediction and the most precise and accurate models of uncertainty.
UR: http://www-personal.engin.umich.edu/$\sim$goovaert/publication.html
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic processes
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting