Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan

Abstract

[1] During the last decade one 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 multi-Gaussian and indicator kriging for modeling probabilistically the spatial distribution of arsenic concentrations in groundwater of southeast Michigan, accounting for arsenic data collected at private residential wells and the hydrogeochemistry of the area. The arsenic data set, which was provided by the Michigan Department of Environmental Quality (MDEQ), includes measurements collected between 1993 and 2002 at 8212 different wells. Factorial kriging was used to filter the short-range spatial variability in arsenic concentration, leading to a significant increase (17-65%) in the proportion of variance explained by secondary information, such as type of unconsolidated deposits and proximity to Marshall Sandstone subcrop. Cross validation of well data shows that accounting for this regional background does not improve the local prediction of arsenic, which reveals the presence of unexplained sources of variability and the importance of modeling the uncertainty attached to these predictions. Slightly more precise models of uncertainty were obtained using indicator kriging. Well data collected in 2004 were compared to the prediction model and best results were found for soft indicator kriging which has a mean absolute error of 5.6 mg/L. Although this error is large with respect to the USEPA standard of 10 mg/L, it is smaller than the average difference (12.53 mg/L) between data collected at the same well and day, as reported in the MDEQ data set. Thus the uncertainty attached to the sampled values themselves, which arises from laboratory errors and lack of information regarding the sample origin, contributes to the poor accuracy of the geostatistical predictions in southeast Michigan.Citation: Goovaerts, P., G. AvRuskin, J. Meliker, M. Slotnick, G. Jacquez, and J. Nriagu (2005), Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan, Water Resour. Res., 41, W07013,

Validity of spatial models of arsenic concentrations in private well water

“…We assessed model validity using an independent validation dataset of 371 private wells. The spatial models include those commonly applied to predict arsenic: a geostatistical model, a nearest neighbor approach, and arithmetic averages in US Public Lands Survey-defined townships and sections (Bates et al, 2004;Chen et al, 1995;Chen et al, 2003;Chen et al, 2004;Goovaerts et al, 2005;Hassan et al, 2003;Serre et al, 2003;Steinmaus et al, 2003). These models were built on a rich dataset of 6050 arsenic measurements collected over a tenyear period in southeastern Michigan.…”

“…Geostatistical Model-The development of the geostatistical model is described in detail elsewhere (Goovaerts et al 2005). Briefly, the geostatistical model capitalizes on the spatial correlation between arsenic values to make predictions at unsampled locations.…”

“…In an Argentinean study of bladder cancer, proxy wells drilled into a common aquifer were used to estimate arsenic concentrations for a past residence (Bates et al, 2004). In Michigan and Bangladesh, geostatistical models were developed to predict arsenic concentrations in well water (Goovaerts et al, 2005;Hassan et al, 2003;Serre et al, 2003). The predictive capacity of these different spatial models, however, has yet to be evaluated.…”

Identification of geospatial variability of fluoride contamination in ground water of Mathura District, Uttar Pradesh, India

“…Assessment of spatial correlation in hydrochemical variables is an important tool in the analysis of groundwater chemistry. This paper investigates the spatial correlation of the Mathura ground water data sets using Kriging method (Franco et al, 2009;Goovaerts, 1997 andGoovaerts et al, 2005). Spatial assessment of groundwater chemistry is important for revealing the correlation between location and the hydrochemical variables in the present study.…”

“…Examples are given of the type of semivariogram found in the study area, while Kriged maps illustrate the spatial relationships found. The semivariogram analysis of the geo-statistical assessment revealed that for the dataset, the spatial correlation has a relatively short range therefore the sill value is difficult to analyze because of a spatial trend in the data, leading to an increase of the semi-variance almost immediately after the sill has been reached (Goovaerts, 1997 andGoovaerts et al, 2005). Thirteen water samples were collected from phreatic aquifer in clean pre-rinsed100 ml plastic (polyethylene) bottles.…”