Showing papers by "C. Annette Johnson published in 2008"

Statistical modeling of global geogenic fluoride contamination in groundwaters.

Abstract: The use of groundwater with high fluoride concentrations poses a health threat to millions of people around the world This study aims at providing a global overview of potentially fluoride-rich groundwaters by modeling fluoride concentration A large database of worldwide fluoride concentrations as well as available information on related environmental factors such as soil properties, geological settings, and climatic and topographical information on a global scale have all been used in the model The modeling approach combines geochemical knowledge with statistical methods to devise a rule-based statistical procedure, which divides the world into 8 different "process regions" For each region a separate predictive model was constructed The end result is a global probability map of fluoride concentration in the groundwater Comparisons of the modeled and measured data indicate that 60-70% of the fluoride variation could be explained by the models in six process regions, while in two process regions only 30% of the variation in the measured data was explained Furthermore, the global probability map corresponded well with fluorotic areas described in the international literature Although the probability map should not replace fluoride testing, it can give a first indication of possible contamination and thus may support the planning process of new drinking water projects

Statistical Modeling of Global Geogenic Arsenic Contamination in Groundwater

Abstract: Contamination of groundwaters with geogenic arsenic poses a major health risk to millions of people. Although the main geochemical mechanisms of arsenic mobilization are well understood, the worldwide scale of affected regions is still unknown. In this study we used a large database of measured arsenic concentration in groundwaters (around 20,000 data points) from around the world as well as digital maps of physical characteristics such as soil, geology, climate, and elevation to model probability maps of global arsenic contamination. A novel rule-based statistical procedure was used to combine the physical data and expert knowledge to delineate two process regions for arsenic mobilization: “reducing” and “high-pH/oxidizing”. Arsenic concentrations were modeled in each region using regression analysis and adaptive neuro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty propagation to produce probability maps. The derived global arsenic models could benefit from more accurate geologic ...

Predicting groundwater arsenic contamination in Southeast Asia from surface parameters

Abstract: Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Although many arsenic-affected areas have been identified in recent years, a systematic evaluation of vulnerable areas remains to be carried out. Here we present maps pinpointing areas at risk of groundwater arsenic concentrations exceeding 10 μg l−1. These maps were produced by combining geological and surface soil parameters in a logistic regression model, calibrated with 1,756 aggregated and geo-referenced groundwater data points from the Bengal, Red River and Mekong deltas. We show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and that the combination of surface parameters is a successful approach to predict groundwater arsenic contamination. Predictions are in good agreement with the known spatial distribution of arsenic contamination, and further indicate elevated risks in Sumatra and Myanmar, where no groundwater studies exist. Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Maps of areas at risk of groundwater arsenic concentrations have been produced by combining geological and surface-soil parameters in a logistic regression model. They show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and indicate elevated risks in Sumatra and Myanmar where no groundwater studies exist.