Arsenic groundwater contamination and its health effects in the state of Uttar Pradesh (UP) in upper and middle Ganga plain, India: a severe danger

This communication presents results of our 2-year survey on groundwater arsenic contamination in three districts Ballia, Varanasi and Gazipur of Uttar Pradesh (UP) in the upper and middle Ganga plain, India. Analyses of 4,780 tubewell water samples revealed that arsenic concentrations in 46.5% exceeded 10 microg/L, in 26.7%, 50 microg/L and in 10% 300 microg/L limits. Arsenic concentrations up to 3,192 microg//L were observed. The age of tubewells (n=1,881) ranged from less than a year to 32 years, with an average of 6.5 years. Our study shows that older tubewells had a greater chance of contamination. Depth of tubewells (n=3,810) varied from 6 to 60.5 m with a mean of 25.75 m. A detailed study in three administrative units within Ballia district, i.e. block, Gram Panchayet, and village was carried out to assess the magnitude of the contamination. Before our survey the affected villagers were not aware that they were suffering from arsenical toxicity through contaminated drinking water. A preliminary clinical examination in 11 affected villages (10 from Ballia and 1 from Gazipur district) revealed typical arsenical skin lesions ranging from melanosis, keratosis to Bowens (suspected). Out of 989 villagers (691 adults, and 298 children) screened, 137 (19.8%) of the adults and 17 (5.7%) of the children were diagnosed to have typical arsenical skin lesions. Arsenical neuropathy and adverse obstetric outcome were also observed, indicating severity of exposure. The range of arsenic concentrations in hair, nail and urine was 137-10,900, 764-19,700 microg/kg, and 23-4,030 microg/L, respectively. The urine, hair and nail concentrations of arsenic correlated significantly (r=0.76, 0.61, and 0.55, respectively) with drinking water arsenic concentrations. The similarity to previous studies on arsenic contamination in West Bengal, Bihar and Bangladesh indicates that people from a significant part of the surveyed areas in UP are suffering and this will spread unless drives to raise awareness of arsenic toxicity are undertaken and an arsenic safe water supply is immediately introduced.     

Ineffectiveness and poor reliability of arsenic removal plants in West Bengal, India

In the recent past, arsenic contamination in groundwater has emerged as an epidemic in different Asian countries, such as Bangladesh, India, and China. Arsenic removal plants (ARP) are one possible option to provide arsenic-safe drinking water. This paper evaluates the efficiency of ARP projects in removing arsenic and iron from raw groundwater, on the basis of our 2-year-long study covering 18 ARPs from 11 manufacturers, both from home and abroad, installed in an arsenic affected area of West Bengal, India, known as the Technology Park Project (TP project). Immediately after installation of ARPs on August 29, 2001, the villagers began using filtered water for drinking and cooking, even though our first analysis on September 13, 2001 found that 10 of 13 ARPs failed to remove arsenic below the WHO provisional guideline value (10 microg/L), while six plants could not achieve the Indian Standard value (50 microg/L). The highest concentration of arsenic in filtered water was observed to be 364 microg/L. Our 2-year study showed that none of the ARPs could maintain arsenic in filtered water below the WHO provisional guideline value and only two could meet the Indian standard value (50 microg/L) throughout. Standard statistical techniques showed that ARPs from the same manufacturers were not equally efficient. Efficiency of the ARPs was evaluated on the basis of point and interval estimates of the proportion of failure. During the study period almost all the ARPs have undergone minor or major modifications to improve their performance, and after our study, 15 (78%) out of 18 ARPs were no longer in use. In this study, we also analyzed urine samples from villagers in the TP project area and found that 82% of the samples contained arsenic above the normal limit.     

Probing the reactivation process of sarin-inhibited acetylcholinesterase with α-nucleophiles: hydroxylamine anion is predicted to be a better antidote with DFT calculations

Inactivation of acetylcholinesterase (AChE) due to inhibition by organophosphorus (OP) compounds is a major threat to human since AChE is a key enzyme in neurotransmission process. Oximes are used as potential reactivators of OP-inhibited AChE due to their α-effect nucleophilic reactivity. In search of more effective reactivating agents, model studies have shown that α-effect is not so important for dephosphylation reactions. We report the importance of α-effect of nucleophilic reactivity towards the reactivation of OP-inhibited AChE with hydroxylamine anion. We have demonstrated with DFT [B3LYP/6-311G(d,p)] calculations that the reactivation process of sarin-serine adduct 2 with hydroxylamine anion is more efficient than the other nucleophiles reported. The superiority of hydroxylamine anion to reactivate the sarin-inhibited AChE with sarin-serine adducts 3 and 4 compared to formoximate anion was observed in the presence and absence of hydrogen bonding interactions of Gly121 and Gly122. The calculated results show that the rates of reactivation process of adduct 4 with hydroxylamine anion are 261 and 223 times faster than the formoximate anion in the absence and presence of such hydrogen bonding interactions. The DFT calculated results shed light on the importance of the adjacent carbonyl group of Glu202 for the reactivation of sarin-serine adduct, in particular with formoximate anion. The reverse reactivation reaction between hydroxylamine anion and sarin-serine adduct was found to be higher in energy compared to the other nucleophiles, which suggests that this α-nucleophile can be a good antidote agent for the reactivation process.     

Condition Assessment of Corrosion-Distressed Reinforced Concrete Buildings Using Fuzzy Logic

A method is presented for obtaining condition index of corrosion distressed RC buildings. Method is developed using concepts of fuzzy logic and it integrates visual inspection with in situ investigations for carbonation and chloride content. Distress manifestations and repair priorities are classified. Condition is related to repair priorities through condition ratings. Repair priorities are fuzzy in nature as they are dependent on interpretation of the inspector. Questionnaire survey is prepared and responses are collected from the experts. Obtained data are used for development of fuzzy membership functions for defined repair priorities. A building can be subdivided into various elements. Observations for various distress manifestations are recorded for each element, using the format proposed. These observations are combined using fuzzy extension technique to obtain individual membership function for each element. Defuzzyfication using center of sum method provides with the combined building condition index (BCI) from elemental membership functions. Obtained BCI provides direct measure of condition and repair needs of the building. Developed methodology is explained through a case study on condition assessment of an academic building.     

Arsenic contamination in sesame and possible mitigation through organic interventions in the lower Gangetic Plain of West Bengal, India

BACKGROUND: The widespread geogenic arsenic contamination of groundwater in the Gangetic Delta of West Bengal, leading to toxicities through the food chain—principally through irrigated rice—drew due attention from researchers. Oilseeds such as sesame might be a remunerative alternative to rice and can be grown with small quantities of contaminated groundwater. RESULTS: The present study was conducted to explore the efficiency of organic matter in reducing arsenic accumulation in sesame. Accumulation of total arsenic in sesame seed and available arsenic in post‐harvest soils varied from 0.08 to 0.58 mg kg^?1 and from 3.87 to 8.89 kg ha^?1, respectively. The organic manures added as soil amendment significantly reduced the accumulation (concentration) of arsenic in sesame seed to a maximum extent of 65.5% (vermicompost), 50% (phosphocompost), 42% (mustard cake) and 40% (farmyard manure (FYM)) compared with the control counterpart. The risk associated with dietary exposure to arsenic‐contaminated sesame oil reached a value of 15.55% of provisional tolerable weekly intake for arsenic at the maximum accumulation of arsenic in sesame oil. CONCLUSION: Substantial accumulation of arsenic in the soil–plant system was found. Risks of exposure to arsenic‐contaminated oil remained considerably high. Irrigation through surface water and organic amendments both significantly reduced arsenic accumulation in sesame. Copyright © 2011 Society of Chemical Industry     

A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine

Engineering with Computers - Ground vibration is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, developing a precise model...     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.