Temporal variation of b value associated with M ��4 earthquakes in the reservoir-triggered seismic environment of the Koyna�CWarna region, Western India

It is generally found that the b values associated with reservoir-triggered seismicity (RTS) are higher than the regional b values in the frequency magnitude relation of earthquakes. In the present study, temporal and spatial variation of b value is investigated using a catalog of 3,000 earthquakes from August 2005 through December 2010 for the Koyna?CWarna region in Western India, which is a classical site of RTS globally. It is an isolated (30?×?20?km²) zone of seismicity where earthquakes of up to M ??5 are found to occur during phases of loading and unloading of the Koyna and Warna reservoirs situated 25?km apart. For the Warna region, it is found that low b values of 0.6?C0.9 are associated with earthquakes of M ??4 during the loading phase. The percentage correlation of the occurrence of an M????4 earthquake with a low b value outside the 1?? or 2?? level is as high as 78?%. A drastic drop in the b value of about 50?% being reported for an RTS site may be an important precursory parameter for short-term earthquake forecast in the future.     

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya,India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps(LSMs) using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model(CNN) and three popular machine learning techniques, i.e., random forest model(RF), artificial neural network model(ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points. A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve(AUC) of the receiver operating characteristics(ROC) curve and statistical methods like root mean square error(RMSE) and mean absolute error(MAE). In both datasets(training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.     

Using spatial statistics to identify the uranium hotspot in groundwater in the mid-eastern Gangetic plain,India

The occurrence of uranium in groundwater is of particular interest due to its toxicological and radiological properties. It has been considered as a relevant contaminant for drinking water even at a low concentration. Uranium is a ubiquitously occurring radionuclide in the environment. Four hundred and fifty-six (456) groundwater samples from different locations of five districts of South Bihar (SB) were collected and concentrations of uranium (U) were analyzed using a light-emitting diode (LED) fluorimetric technique. Uranium concentrations in groundwater samples varied from 0.1 µg l^?1 to 238.2 µg l^?1 with an average value of 12.3 µg l^?1 in five districts of Bihar in the mid-eastern Gangetic plain. This study used hot spot spatial statistics to identify the distribution of elevated uranium concentration in groundwater. The hypothesis whether spatial distribution of high value and low value of U is more likely spatially clustered due to random process near a uranium hotspot in groundwater was tested based on z score and Getis-Ord Gi* statistics. The method implemented in this study, can be utilized in the field of risk assessment and decision making to locate potential areas of contamination.     

Estimation of snow ablation under a dust layer covering a wide range of albedo

Ablation processes of snow under a thin dust cover are complicated compared with those under a thick cover, mainly owing to the effects of aggregation (redistribution) of dust particles on the conditions of surface melting. Aggregation of dust particles causes the snow surface to brighten after the initial dust configuration, thus affecting the relationship between initial dust concentration and surface albedo. In order to estimate snow ablation rate under a thin dust cover, we used a composite energy balance model in which the surface albedo is taken as a measured input variable. The estimated results of snow ablation agreed reasonably well with the observation, considering the measurement errors inherited in the snow depressions. Comparison of the two cases, that is, one considering the aggregation of dust particles (observation: albedo variable) and the other without aggregation (assumption: albedo constant), showed that the ablation rates were noticeably lower on the former case. This suggests that the aggregation of dust particles induces a reduction of snow ablation. Copyright © 2002 John Wiley & Sons, Ltd.     

Sustainable Management of Electronic Waste (e-Waste)

Electronic waste or e-waste is one of the fastest growing areas of the international waste stream and is increasing at a much higher rate than all other waste streams. Fast growing electronics industries arising from the demands of information and communication technologies around the world coupled with the rapid product obsolescence and lack of end-of-life management options, have all led to the unsustainable management of the waste stream. Computers are manufactured from over 1,000 materials, many of which are toxic, and they contribute significantly to the e-waste stream, which is estimated to be ca. 20 to 50 million tonnes annually. Design for environment cleaner production, extended producer responsibility, standards and labeling, product stewardship, recycling and remanufacturing are some of the practices adopted by various countries around the world to deal with the e-waste stream. An overview of these practices is presented and the manner in which they contribute to the sustainable management of e-waste is discussed.     

Transboundary aquifers: a global program to assess, evaluate, and develop policy

Transboundary aquifers are as important a component of global water resource systems as are transboundary rivers; yet, their recognition in international water policy and legislation is very limited. Existing international conventions and agreements barely address aquifers and their resources. To rectify this deficiency, the International Association of Hydrogeologists and UNESCO's International Hydrological Programme have established the Internationally Shared (transboundary) Aquifer Resource Management (ISARM) Programme. This multiagency cooperative program has launched a number of global and regional initiatives. These are designed to delineate and analyze transboundary aquifer systems and to encourage riparian states to work cooperatively toward mutually beneficial and sustainable aquifer development. The agencies participating in ISARM include international and regional organizations (e.g., Organization of American States, United Nations Environment Programme, United Nations Economic Commission for Europe, Food and Agriculture Organization, and South African Development Community). Using outputs of case studies, the ISARM Programme is building scientific, legal, environmental, socioeconomic, and institutional guidelines and recommendations to aid sharing nations in the management of their transboundary aquifers. Since its start in 2000, the program has completed inventories of transboundary aquifers in the Americas and Africa, and several ISARM case studies have commenced.     

Three-dimensional hybrid modelling of soil-structure interaction

A three-dimensional hybrid model for the analysis of soil-structure interaction under dynamic conditions is developed which takes advantage of the desirable features of the finite element and substructure methods and which minimizes their undesirable features. The modelling is achieved by partitioning the total soil-structure system into a near-field and a far-field with a hemispherical interface. The near-field, which consists of the structure to be analysed and a finite region of soil around it, is modelled by finite elements. The semi-infinite far-field is modelled by distributed impedance functions at the interface which are determined by system identification methods. Numerical results indicate that the proposed model makes possible realistic and economical assessment of three-dimensional soil-structure interaction for both surface and embedded structures.     

Elucidation of the Alum Shale kerogen structure using a multi-disciplinary approach

The significance and validity of integrating data obtained from a variety of analytical techniques to understand, elucidate and model kerogen's complex chemical structure is reported here using degradative (open and closed system pyrolysis, chemical oxidation), non-degradative (¹³C CP/MAS NMR) and optical (incident white light and blue light) methods. Seven Cambrian Alum Shale samples, ranging in maturity from immature to post-mature with respect to petroleum generation, were studied and were chosen for their simple geological history, uniform organic matter type and high organic carbon content. The Alum Shale kerogens, which primarily consist of algal organic matter, liberate low molecular weight gaseous and aromatic compounds on pyrolysis and give mostly branched dicarboxylic acids on chemical oxidation. ¹³C NMR spectroscopy shows that the Alum Shale kerogens are anomalously rich in oxygen-bearing functional groups (such as C = O, ArCO, CHO, CH_xO), most of which apparently remain intact within the kerogen macro-molecule (KMM) through the diagenetic and catagenetic stages. Fragments released by different degradative techniques are quantified and the aromaticity (f_a), O/C and relative proportions of various carbon types estimated by ¹³C NMR. A synthesis of these data has allowed us to better understand the chemistry of the Alum Shale kerogen.     

Identification of trends in rainfall,rainy days and 24 h maximum rainfall over subtropical Assam in Northeast India

Trends in rainfall, rainy days and 24 h maximum rainfall are investigated using the Mann-Kendall non-parametric test at twenty-four sites of subtropical Assam located in the northeastern region of India. The trends are statistically confirmed by both the parametric and non-parametric methods and the magnitudes of significant trends are obtained through the linear regression test. In Assam, the average monsoon rainfall (rainy days) during the monsoon months of June to September is about 1606 mm (70), which accounts for about 70% (64%) of the annual rainfall (rainy days). On monthly time scales, sixteen and seventeen sites (twenty-one sites each) witnessed decreasing trends in the total rainfall (rainy days), out of which one and three trends (seven trends each) were found to be statistically significant in June and July, respectively. On the other hand, seventeen sites witnessed increasing trends in rainfall in the month of September, but none were statistically significant. In December (February), eighteen (twenty-two) sites witnessed decreasing (increasing) trends in total rainfall, out of which five (three) trends were statistically significant. For the rainy days during the months of November to January, twenty-two or more sites witnessed decreasing trends in Assam, but for nine (November), twelve (January) and eighteen (December) sites, these trends were statistically significant. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the well-reported climatic warming in monsoon and post-monsoon seasons may have implications for human health and water resources management over bio-diversity rich Northeast India.