Bayesian modeling approach for characterizing groundwater arsenic contamination in the Mekong River basin

Abstract

In the Mekong River basin, groundwater from tube-wells is a major drinking water source. However, arsenic (As) contamination in groundwater resources has become a critical issue in the watershed. In this study, As species such as total As (As-TOT), As(III), and As(V), were monitored across the watershed to investigate their characteristics and inter-relationships with water quality parameters, including pH and redox potential (Eh). The data illustrated a dramatic change in the relationship between As-TOT and Eh over a specific Eh range, suggesting the importance of Eh in predicting As-TOT. Thus, a Bayesian change-point model was developed to predict As-TOT concentrations based on Eh and pH, to determine changes in the As-TOT-Eh relationship. The model captured the Eh change-point (similar to-100 +/- 15 mV ), which was compatible with the data. Importantly, the inclusion of this change-point in the model resulted in improved model fit and prediction accuracy; As-TOT concentrations were strongly negatively related to Eh values higher than the change-point. The process underlying this relationship was subsequently posited to be the reductive dissolution of mineral oxides and As release. Overall, As-TOT showed a weak positive relationship with Eh at a lower range, similar to those commonly observed in the Mekong River basin delta. It is expected that these results would serve as a guide for establishing public health strategies in the Mekong River Basin. (C) 2015 Elsevier Ltd. All rights reserved.