title: 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 (AsTOT), 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 AsTOT and Eh over a specific Eh range, suggesting the importance of Eh in predicting AsTOT. Thus, a Bayesian change-point model was developed to predict AsTOT concentrations based on Eh and pH, to determine changes in the AsTOT-Eh relationship. The model captured the Eh change-point (∼-100±15mV), which was compatible with the data. Importantly, the inclusion of this change-point in the model resulted in improved model fit and prediction accuracy; AsTOT 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, AsTOT 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.
authors: Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, Ann Arbor, MI 48108, United States.
authors: YoonKyung
authors: Y
authors: Cha
authors: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors: Young Mo
authors: YM
authors: Kim
authors: Center for Water Resource Cycle Research, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea.
authors: Jae-Woo
authors: JW
authors: Choi
authors: Department of Environmental Engineering, Dong-A University, Busan 604-714, Republic of Korea. Electronic address: suthisuthi@gmail.com.
authors: Suthipong
authors: S
authors: Sthiannopkao
authors: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea. Electronic address: khcho@unist.ac.kr.
authors: Kyung Hwa
authors: KH
authors: Cho
mesh_terms: D001151:Arsenic / Q000032:analysis*; D001499:Bayes Theorem; D002160:Cambodia; D060766:Drinking Water / Q000737:chemistry; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D060587:Groundwater / Q000737:chemistry*; D006863:Hydrogen-Ion Concentration; D007812:Laos; D010084:Oxidation-Reduction; D045483:Rivers; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014881:Water Supply; D060986:Water Wells
chemical_list: D060766:Drinking Water; D014874:Water Pollutants, Chemical; D001151:Arsenic
keywords: Arsenic (As) contamination; Bayesian change-point model; Drinking water source; Groundwater; Linear model; Mekong River basin