Groundwater arsenic contamination and its health effects in India

During a 28-year field survey in India (1988–2016), groundwater arsenic contamination and its health effects were registered in the states of West Bengal, Jharkhand, Bihar and Uttar Pradesh in the Ganga River flood plain, and the states of Assam and Manipur in the flood plain of Brahamaputra and Imphal rivers. Groundwater of Rajnandgaon village in Chhattisgarh state, which is not in a flood plain, is also arsenic contaminated. More than 170,000 tubewell water samples from the affected states were analyzed and half of the samples had arsenic >10 μg/L (maximum concentration 3,700 μg/L). Chronic exposure to arsenic through drinking water causes various health problems, like dermal, neurological, reproductive and pregnancy effects, cardiovascular effects, diabetes mellitus, diseases of the respiratory and gastrointestinal systems, and cancers, typically involving the skin, lungs, liver, bladder, etc. About 4.5% of the 8,000 children from arsenic-affected villages of affected states were registered with mild to moderate arsenical skin lesions. In the preliminary survey, more than 10,000 patients were registered with different types of arsenic-related signs and symptoms, out of more than 100,000 people screened from affected states. Elevated levels of arsenic were also found in biological samples (urine, hair, nails) of the people living in affected states. The study reveals that the population who had severe arsenical skin lesions may suffer from multiple Bowens/cancers in the long term. Some unusual symptoms, such as burning sensation, skin itching and watering of eyes in the presence of sun light, were also noticed in arsenicosis patients.     

Identification of pre-seismic anomalies of soil radon-222 signal using Hilbert–Huang transform

Concentration of Rn-222 in soil has been monitored continuously at Ravangla in the Sikkim Himalayan Region of eastern India for about 7 months from October 2015 to May 2016 to detect earthquake-induced anomalies. The recorded data clearly show that various physical and meteorological parameters influence the soil radon concentration, leading to very complex soil Rn-222 time series. The components due to such external influences have been removed from the present time series, and Hilbert–Huang transform (HHT) applied for analysis of the data. Two radon anomalies caused due to earthquakes of magnitude M _b = 5.0 that occurred on 19 November 2015 and 5 April 2016 within an epicentral distance of 500 km from the monitoring station have been identified on the soil Rn-222 time series. These two precursory anomalies occurred 9 and 10 days, respectively, before the occurrence of the earthquakes. The absence of spurious signals or missing anomalies demonstrates that HHT is advantageous for analysis of nonlinear non-stationary data, and hence, it is a promising technique to analyse soil radon behaviour for predicting the possibility of occurrence of earthquakes.     

Reliability Analysis of Excavation Induced Basal Heave

The present study deals with the reliability analysis of basal heave caused by excavation considering uncertainty in the soil properties. The case study considered in the present work has been analyzed deterministically by Hsieh et al. (Can Geotech J 45:788–799, 2008). Taiwan building code is adopted in the method for analyzing the basal heave failure. The random variables (undrained shear strength and total unit weight of clay) are assumed to be normally distributed and uncorrelated. A series of parametric studies have been conducted to calculate the reliability index on the basis of the matrix formulation for the second moment method by Hasofer and Lind (J Eng Mech ASCE 100(1):111–121, 1974) considering different coefficient of variation of undrained shear strength and total unit weight of clay layers. It has been found that for a particular value of coefficient of variation of total unit weight, the reliability index with respect to occurrence of basal heave failure decreases with increase in the coefficient of variation of undrained shear strength. Moreover, the reliability index also decreases when the coefficient of variation of total unit weight increases. It has also been found that the probability of basal heave failure is lower with respect to factor of safety equals to 1.2, as compared to factor of safety equals to 1.0. Sensitivity analysis shows that the undrained shear strength of the bottommost layer and total unit weight of the second layer are the most significant random variables affecting the reliability index. Guidelines are provided for reliability based design where, for ‘target’ reliability index of 2.5 and 3.0, the factor of safety can be chosen such that all the related uncertainties are taken into account, especially with regard to undrained shear strength of the bottommost layer and total unit weight of the second layer. Design guidelines have been provided for this purpose.     

Mitigating parameter bias in hydrological modelling due to uncertainty in covariates

The uncertainty in hydrological model covariates, if ignored, introduces systematic bias in the parameters estimated. We introduce here a method to determine the true value of parameters given uncertainty in model inputs. This method, known as simulation extrapolation (SIMEX) operates on the basis of an empirical relationship between parameters and the level of input noise (or uncertainty). The method starts by generating a series of alternate model inputs by artificially adding white noise in increasing multiples of the known error variance. The resulting parameter sets allow us to formulate an empirical relationship between their values and the level of noise present. SIMEX is based on theory that the trend in alternate parameters can be extrapolated back to the notional error free zone.

We illustrate the strength of SIMEX in improving skills of predictive models that use uncertain sea surface temperature anomaly (SSTA) data over the NINO3 region as predictor to the southern oscillation index (SOI), an alternate measure of the strength of the El Nino southern oscillation. Our hypothesis is that the higher magnitude of noise in the pre 1960 data period introduces bias to model parameters where SSTA is the input variable. The relatively error invariant southern oscillation index (SOI) is regressed over SSTA and calibrated using a subset of the series from 1900 to 1960. We validate the resulting models using the less erroneous 1960–2003 data period. Overall the application of SIMEX is found to reduce the residual predictive errors during the validation period.     

Clay mineralogy of archaeological soil: an approach to paleoclimatic and environmental reconstruction of the archaeological sites of the Paharpur area,Badalgacchi upazila,Naogaon district,Bangladesh

A geo-archaeological examination was carried out in and around the Paharpur area, Badalgacchi upazila of Naogaon District in the north-western part of Bangladesh. Clay mineralogy and global paleo-temperature curves were used to determine the paleoclimatic and depositional environmental conditions of the Holocene archaeological soils (Barind clay residuum) of the Paharpur archaeological spot. Clay mineralogical data show that the dominant clay mineral is illite with subordinate amounts of kaolinite and chlorite. The high illite and low kaolinite content implies a marine or estuarine deposit. The clay mineral ratio log diagram, constructed by plotting the 7–10°Å integrated intensity ratios versus depth, supports this assumption. The presence of illite, together with quartz and feldspar, suggests high detrital input under generally cool to temperate and dry climates. Chlorite is another common by-product of weathering reactions with low hydrolysis, which is typical for cool to temperate and dry climates. The results suggest the existence of a marine–estuarine influence under a generally cool to temperate and dry climates around Paharpur and the surrounding region throughout the Pala dynasty around mid-century 700 a.d. to early 1100 a.d. Furthermore, this observation is consistent with the global paleo-temperature curves.     

Long-term trend analysis of water table using ‘MAKESENS’ model and sustainability of groundwater resources in drought prone Barind area,NW Bangladesh

In Bangladesh, agriculture plays a major role in the national economy. In the drought prone Barind area in NW Bangladesh, cropping intensity has increased almost double since late eighties of last century (from 1985) because of the introduction of groundwater irrigation. Long-term behavior of groundwater table (GWT) in the drought prone Barind area has been studied using MAKESENS model in the wake of massive installation of tube-wells. The study reveals that the maximum and minimum depths to GWT during 1991-2010 show on average declining trend of 4.51 m and 4.73 m. The long-term prediction for the period of 2020-50 assuming the current rate of groundwater withdrawal is that the declining trend will be 1.16 to 1.59 and 1.07 to 1.82 times more for maximum and minimum groundwater depths respectively in comparison to the present. The rigorous exploitation of groundwater for irrigation, decreasing rainfall and surface geological attributes lead towards declining trend of GWT. This will hamper the country’s food security and ultimately threaten its socio-economic sustainability. So the appropriate strategies for the management of groundwater resource on a sustainable basis should be the priority for maintaining agricultural productivity.     

On the Characteristics of Wave Propagation in a Magnetized Dusty Plasma with Streaming of Electrons and Ions

Propagation of waves in a magnetized dusty plasma are studied for all the range of values of ion-cyclotron frequency, and having streams of electrons and ions. The dispersion relation is obtained for the waves propagating through the dusty plasma and analysed for different modes of propagation for relative abundance of dust in the plasma. It is observed that abundance of dust, streaming motions of electrons and ions have an important influence on the propagation of waves in the dusty plasma particularly when the phase velocity of the wave is in the low frequency region. This revised version was published online in July 2006 with corrections to the Cover Date.     

Application of artificial intelligence techniques in automatic hull form generation

The paper describes a methodology for automatic hull form generation of ships with some desired performances using artificial intelligence techniques. The whole implementation process is divided into three main components. First of all the half-breadth weight matrices are generated that would provide population with pre-fitness by examining the relationship of the hull form with the principal dimensions of a number of existing vessels. Secondly, breadth and draft are adjusted using Neural Network Concepts. Breadth, draft, length, displacement and speed of the ship are very related terms and relations among them create some constraints. Neural Networks solve these constraints and adjust the parameters. Finally, Genetic Algorithm is used for searching the exact solution by examining several generations. For this, the algorithms need to measure fitness for every population in every generation. Unfortunately, GA doesn't guarantee fairness of the surface of the hull form, which can't be ignored. So, for every population especially for newborn population fairing techniques would be used. However, being complex geometric shape, hull form surface can't be faired using least-square or other single equation fitting techniques. Fairness is done taking pair-wise points using B-spline functions [CAD 20(1988)].