Validity of spatial models of arsenic concentrations in private well water

Abstract

Objective Arsenic is a pervasive contaminant in underground aquifers worldwide, yet documentation of health effects associated with low-to-moderate concentrations (<100 μg/L) has been stymied by uncertainties in assessing long-term exposure. A critical component of assessing exposure to arsenic in drinking water is the development of models for predicting arsenic concentrations in private well water in the past; however, these models are seldom validated. The objective of this paper is to validate alternative spatial models of arsenic concentrations in private well water in southeastern Michigan. Methods From 1993 to 2002, the Michigan Department of Environmental Quality analyzed arsenic concentrations in water from 6050 private wells. This dataset was used to develop several spatial models of arsenic concentrations in well water: proxy wells based on nearest-neighbor relationships, averages across geographic regions, and geostatistically derived estimates based on spatial correlation and geologic factors. Output from these models was validated using arsenic concentrations measured in 371 private wells from 2003 to 2006. Results The geostatisical model and nearest-neighbor approach outperformed the models based on geographic averages. The geostatistical model produced the highest degree of correlation using continuous data (Pearson's r=0.61; Spearman's rank ρ=0.46) while the nearest-neighbor approach produced the strongest correlation ( κ weighted=0.58) using an a priori categorization of arsenic concentrations (<5, 5–9.99, 10–19.99, ⩾20 μg/L). When the maximum contaminant level was used as a cut-off in a two-category classification (<10, ⩾10 μg/L), the nearest-neighbor approach and geostatistical model had similar values for sensitivity (0.62–0.63), specificity (0.80), negative predictive value (0.85), positive predictive value (0.53), and percent agreement (75%). Discussion This validation study reveals that geostatistical modeling and nearest-neighbor approaches are effective spatial models for predicting arsenic concentrations in private well water. Further validation analyses in other regions are necessary to indicate how widely these findings may be generalized.