Groundwater arsenic contamination in Brahmaputra river basin: a water quality assessment in Golaghat (Assam), India

Distribution of arsenic (As) and its compound and related toxicology are serious concerns nowadays. Millions of individuals worldwide are suffering from arsenic toxic effect due to drinking of As-contaminated groundwater. The Bengal delta plain, which is formed by the Ganga?CPadma?CMeghna?CBrahmaputra river basin, covering several districts of West Bengal, India, and Bangladesh is considered as the worst As-affected alluvial basin. The present study was carried out to examine As contamination in the state of Assam, an adjoining region of the West Bengal and Bangladesh borders. Two hundred twenty-two groundwater samples were collected from shallow and deep tubewells of six blocks of Golaghat district (Assam). Along with total As, examination of concentration levels of other key parameters, viz., Fe, Mn, Ca, Na, K, and Mg with pH, total hardness, and SO $_{4}^{2-}$ , was also carried out. In respect to the permissible limit formulated by the World Health Organization (WHO; As 0.01 ppm, Fe 1.0 ppm, and Mn 0.3 ppm for potable water), the present study showed that out of the 222 groundwater samples, 67%, 76.4%, and 28.5% were found contaminated with higher metal contents (for total As, Fe, and Mn, respectively). The most badly affected area was the Gamariguri block, where 100% of the samples had As and Fe concentrations above the WHO drinking water guideline values. In this block, the highest As and Fe concentrations were recorded 0.128 and 5.9 ppm, respectively. Tubewell water of depth 180 ± 10 ft found to be more contaminated by As and Fe with 78% and 83% of the samples were tainted with higher concentration of such toxic metals, respectively. A strong significant correlation was observed between As and Fe (0.697 at p < 0.01), suggesting a possible reductive dissolution of As?CFe-bearing minerals for the mobilization of As in the groundwater of the region.     

Impact of raking and bioturbation-mediated ecological manipulation on sediment–water phosphorus diagenesis: a mesocosm study supported with radioactive signature

The study examined the impact of raking and fish bioturbation on modulating phosphorus (P) concentrations in the water and sediment under different trophic conditions. An outdoor experiment was set to monitor physicochemical and microbiological parameters of water and sediment influencing P diagenesis. A pilot study with radioactive ³²P was also performed under the agency of raking and bacteria (Bacillus sp.). Raking was more effective in release of P under unfertilized conditions by significantly enhancing orthophosphate (35%) and soluble reactive phosphate (31.8%) over respective controls. Bioturbation increased total and available P in sediments significantly as compared to control. The rates of increase were higher in the unfertilized conditions (17.6–28.4% for total P and 12.2 to 23.2% for available P) than the fertilized ones (6.5–12.4% for total P and 9.1 to 15% for available P). The combined effects of raking and bioturbation on orthophosphate and soluble reactive phosphate were also stronger under unfertilized state (54.5 and 81.8%) than fertilized ones (50 and 70%). The tracer signature showed that coupled action of introduced bacteria and repeated raking resulted in 59.2, 23 and 16% higher counts of radioactive P than the treatments receiving raking once, repeated raking and bacteria inoculation, respectively. Raking alone or in sync with bioturbation exerted pronounced impact on P diagenesis through induction of coupled mineralization and nutrient release. It has significant implication for performing regular raking of fish-farm sediments and manipulation of bottom-grazing fish to regulate mineralization of organic matter and release of obnoxious gases from the system. Further, they synergistically can enhance the buffering capacity against organic overload and help to maintain aquatic ecosystem health.

     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.