Groundwater Arsenic Contamination and Availability of Safe Water for Drinking in Middle Ganga Plain in India

2019 ◽  
pp. 1-41
Author(s):  
Sudarsan Sahu ◽  
Dipankar Saha
Keyword(s):  
Arsenic Contamination ◽  
Ganga Plain ◽  
Safe Water ◽  
Groundwater Arsenic ◽  
Middle Ganga Plain

scholarly journals Effect of arsenic on Amaranthus gangeticus

2018 ◽  
Vol 53 (4) ◽  
pp. 259-264
Author(s):  
MZ Hossain ◽  
Sushmita Dey ◽  
MS Islam
Keyword(s):  
Irrigation Water ◽  
Crop Production ◽  
Growth Yield ◽  
Dry Weight ◽  
Arsenic Contamination ◽  
Pot Experiment ◽  
Production Potential ◽  
Groundwater Arsenic ◽  
Major Nutrients

Groundwater arsenic contamination has become a threat to the crop production potential in the soils of vast areas of Bangladesh. Situation is grave in some districts of the country, particularly the southern part. A pot experiment was conducted to investigate the effects of arsenic treated irrigation water (0, 1, 2, 5 and 10 mgL-1), where a total of ten (10) irrigations were provided thus the treatments received 0, 10, 20, 50, and 100 mg arsenic (As) pot-1. Effects of applied levels of arsenic on Amaranthus gangeticus (Lal shak) were evaluated in terms of the growth, yield, major nutrients’ content, and their translocation in the plant. As treatments significantly reduced (p≤0.05) the dry weight of shoot and root by 19.31% and 44.03% respectively. Both total and available concentrations of nitrogen (N), potassium (K) and sulfur (S) were significantly (p≤ 0.05) suppressed by the As treatments, while only higher three doses significantly (p≤ 0.05) affected both levels of concentrations of phosphorus (P), calcium (Ca) and magnesium (Mg). Translocation coefficients for soil to root for P, K, S, and Mg were significantly reduced (p≤ 0.05), while translocation coefficients for root to shoot were significantly increased (p≤ 0.05) for K and S by 5 and 10 mgL-1 of arsenic treatments.Bangladesh J. Sci. Ind. Res.53(4), 259-264, 2018

scholarly journals Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Manish Pandey ◽  
Aman Arora ◽  
Alireza Arabameri ◽  
Romulus Costache ◽  
Naveen Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Ground Subsidence ◽  
Ensemble Model ◽  
Ganga Plain ◽  
Humid Climate ◽  
Area Index ◽  
Flood Susceptibility ◽  
Middle Ganga Plain

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

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