Predicting the risk of groundwater arsenic contamination in drinking water wells.

Arsenic (As)-contaminated groundwater is a global concern with potential detrimental effects on the health of hundreds of millions of people worldwide. However, the extent of this problem may be more severe than anticipated, as many wells have not been tested and may contain unsafe-level of As. An optimized statistical regression model was developed to predict the probability of geogenic high As groundwater (As > 10 μg/L) in this study. Easily obtained hydrogeochemical and geological parameters that are significantly related to As geochemical behaviors were selected as explanatory variables in the model. The results indicate that pH, Cl − , HCO 3 − , SO 4 2− , and NO 3 − concentrations, stratigraphic information, and well depth are excellent predictors of As exposure in the Datong Basin, China. Predicted unsafe wells correspond well with the known distribution of high As groundwater in the Datong Basin. The successful application of a data set from Bangladesh also demonstrated the applicability and credibility of this proposed method. [ABSTRACT FROM AUTHOR]