Mass fluxes of dissolved arsenic discharging to the Meghna River are sufficient to account for the mass of arsenic in riverbank sediments

Hotspots of Dissolved Arsenic Generated from Buried Silt Layers along Fluctuating Rivers

Previous studies along the banks of the tidal Meghna River of the Ganges-Brahmaputra-Meghna Delta demonstrated the active sequestration of dissolved arsenic (As) on newly formed iron oxide minerals (Fe(III)-oxides) within riverbank sands. The sand with high solid-phase As (>500 mg/kg) was located within the intertidal zone where robust mixing occurs with oxygen-rich river water. Here we present new evidence that upwelling groundwater through a buried silt layer generates the dissolved products of reductive dissolution of Fe(III)-oxides, including As, while mobilization of DOC by upwelling groundwater prevents their reconstitution in the intertidal zone by lowering the redox state. A three end-member conservative mixing model demonstrated mixing between riverbank groundwater above the silt layer, upwelling groundwater through the silt layer, and river water. An electrochemical mass balance model confirmed that Fe(III)-oxides were the primary electron acceptor driving the oxidation of DOC sourced from sediment organic carbon in the silt. Thus, the presence of an intercalating silt layer in the riverbanks of tidal rivers can represent a biogeochemical hotspot of As release while preventing its retention in the hyporheic zone.

A Low-Cost Programmable Reversing Flow Column Apparatus for Investigating Mixing Zones

This note describes the development and testing of a novel, programmable reversing flow 1D (R1D) experimental column apparatus designed to investigate reaction, sorption, and transport of solutes in aquifers within dynamic reversing flow zones where waters with different chemistries mix. The motivation for constructing this apparatus was to understand the roles of mixing and reaction on arsenic discharging through a tidally fluctuating riverbank. The apparatus can simulate complex transient flux schedules similar to natural flow regimes The apparatus uses an Arduino microcontroller to control flux magnitude through two peristaltic pumps. Solenoid valves control flow direction from two separate reservoirs. In-line probes continually measure effluent electrical conductance, pH, oxidation-reduction potential, and temperature. To understand how sensitive physical solute transport is to deviations from the real hydrograph of the tidally fluctuating river, two experiments were performed using: (1) a simpler constant magnitude, reversing flux direction schedule (RCF); and (2) a more environmentally relevant variable magnitude, reversing flux direction schedule (RVF). Wherein, flux magnitude was ramped up and down according to a sine wave. Modeled breakthrough curves of chloride yielded nearly identical dispersivities under both flow regimes. For the RVF experiment, Peclet numbers captured the transition between diffusion and dispersion dominated transport in the intertidal interval. Therefore, the apparatus accurately simulated conservative, environmentally relevant mixing under transient, variable flux flow regimes. Accurately generating variable flux reversing flow regimes is important to simulate the interaction between flow velocity and chemical reactions where Brownian diffusion of solutes to solid-phase reaction sites is kinetically limited.

Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches

Deterioration of groundwater quality is a long-term incident which leads unending vulnerability of groundwater. The present work was carried out in Murshidabad District, West Bengal, India to assess groundwater vulnerability due to elevated arsenic (As) and other heavy metal contamination in this area. The geographic distribution of arsenic and other heavy metals including physicochemical parameters of groundwater (in both pre-monsoon and post-monsoon season) and different physical factors were performed. GIS-machine learning model such as support vector machine (SVM), random forest (RF) and support vector regression (SVR) were used for this study. Results revealed that, the concentration of groundwater arsenic compasses from 0.093 to 0.448 mg/L in pre-monsoon and 0.078 to 0.539 mg/L in post-monsoon throughout the district; which indicate that all water samples of the Murshidabad District exceed the WHO's permissible limit (0.01 mg/L). The GIS-machine learning model outcomes states the values of area under the curve (AUC) of SVR, RF and SVM are 0.923, 0.901 and 0.897 (training datasets) and 0.910, 0.899 and 0.891 (validation datasets), respectively. Hence, "support vector regression" model is best fitted to predict the arsenic vulnerable zones of Murshidabad District. Then again, groundwater flow paths and arsenic transport was assessed by three dimensions underlying transport model (MODPATH). The particles discharging trends clearly revealed that the Holocene age aquifers are major contributor of As than Pleistocene age aquifers and this may be the main cause of As vulnerability of both northeast and southwest parts of Murshidabad District. Therefore, special attention should be paid on the predicted vulnerable areas for the safeguard of the public health. Moreover, this study can help to make a proper framework towards sustainable groundwater management.