An unusual occurrence of coesite at the Lonar crater,India

Coesite has been identified within ejected blocks of shocked basalt at Lonar crater, India. This is the first report of coesite from the Lonar crater. Coesite occurs within SiO₂ glass as distinct ~30 μm spherical aggregates of “granular coesite” identifiable both with optical petrography and with micro‐Raman spectroscopy. The coesite+glass occurs only within former silica amygdules, which is also the first report of high‐pressure polymorphs forming from a shocked secondary mineral. Detailed petrography and NMR spectroscopy suggest that the coesite crystallized directly from a localized SiO₂ melt, as the result of complex interactions between the shock wave and these vesicle fillings.     

Optimizing the Determination of Roughness Parameters for Model Urban Canopies

We present an objective optimization procedure to determine the roughness parameters for very rough boundary-layer flow over model urban canopies. For neutral stratification the mean velocity profile above a model urban canopy is described by the logarithmic law together with the set of roughness parameters of displacement height d, roughness length \(z_0\), and friction velocity \(u_*\). Traditionally, values of these roughness parameters are obtained by fitting the logarithmic law through (all) the data points comprising the velocity profile. The new procedure generates unique velocity profiles from subsets or combinations of the data points of the original velocity profile, after which all possible profiles are examined. Each of the generated profiles is fitted to the logarithmic law for a sequence of values of d, with the representative value of d obtained from the minima of the summed least-squares errors for all the generated profiles. The representative values of \(z_0\) and \(u_*\) are identified by the peak in the bivariate histogram of \(z_0\) and \(u_*\). The methodology has been verified against laboratory datasets of flow above model urban canopies.     

Rare Earth and Other Trace Element Content of NRCC HISS-1 Sandy Marine Sediment Reference Material

The National Research Council (NRC), Ottawa, Canada sandy marine sediment reference material HISS-1 was characterised for thirty-seven trace elements by neutron activation optimised irradiation, cooling and counting protocols using the low power Miniature Neutron Source Reactor (MNSR) as a neutron source. This INAA methodology quantified twenty additional elements including ten rare earth (Ce, Dy, Eu, Ho, La, Lu, Nd, Sm, Tb and Yb) and ten other elements (Ba, Br, Cs, Ga, Hf, Rb, Sc, Ta, Th and Zr) missing in the final NRCC certification. A large number of values produced by different irradiation schemes together with the use of certified reference materials in the quantification step that showed good precision, provided confidence in the results. The reliability of the REE data was checked by plotting chondrite-normalised graphs.     

A critical review on waste paper sorting techniques

Efficient waste paper recycling has a significant role in the sustainable environment. Recyclable waste paper as a fundamental ingredient of municipal solid wastes (MSWs) is indeed an “urban ore”. Waste papers are considered as the solid recovered fuel which is recovered from MSW. Recyclable waste papers are segregated into various grades to produce high-quality products. Moreover, sorted paper streams save energy, chemicals, and water, as well as reduce sludge and rejects. Information technology is widely integrated with the waste management industry into its operations such as recycling, reuse, segregating based on categories and so on. This review article focuses on the life cycle of waste paper and existing waste paper sorting techniques. In the paper industry, many types of sensors are used in different mechanical and optical waste paper sorting systems. Such sensors include lignin, gloss, stiffness, mid-infrared, infrared, and color sensors. In this review, also described the effectiveness of different waste paper sorting systems, and finally, recommended appropriate waste paper sorting techniques based on effectiveness and low-cost implementation.     

Search for TeV gamma-rays from Geminga pulsar

We present the results of observation of the Geminga pulsar carried out in the TeV energy band during the 6 year period spanning 2000–2006 using the Pachmarhi Array of Cherenkov Telescopes (PACT). A long stretch of data, new computer codes and the “Tempo” package have been used in the present analysis. We have searched for evidence of pulsed emission of γ-rays from the Geminga pulsar using the post-glitch pulsar elements obtained by Jackson and Halpern from X-ray/γ-ray satellite data. We do not see any significant evidence for pulsed emission from the Geminga pulsar at a threshold energy of 825 GeV. In this paper we present our results on the light curve in the TeV energy band, set an upper limit on the time averaged flux of γ-rays, and compare our results with other ground based observations.     

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd.     

Contrasting Influence of Geology on E. coli and Arsenic in Aquifers of Bangladesh

Arsenic in groundwater has been a concern in South and Southeast Asia for more than a decade. We explore here the possibility that hydrogeologic factors recently shown to influence the distribution of arsenic might also affect the level of contamination of shallow (<20 m) wells with microbial pathogens. A total of 96 shallow tube wells in two nearby villages of Bangladesh were surveyed during the wet and dry seasons, along with 55 deeper wells in neighboring villages. One of the two villages is located in a particularly sandy environment where recharge is rapid and shallow wells contain little arsenic. Shallow aquifers in the other village are capped with an impermeable clay layer, recharge is an order of magnitude slower, and arsenic levels are high. The fecal indicator E. coli was detected in 43% of shallow wells, compared with 12% of deeper wells. More shallow wells contained E. coli during the wet season (61%) than during the dry season (9%). In the wet season, a higher proportion of shallow wells in the village with low arsenic levels (72%) contained E. coli compared with the village having high arsenic levels (43%). Differences in arsenic and E. coli distributions between the two sites are likely due to the differences in permeability of near‐surface sediments although differences in average well‐depth between the two villages (9 ± 4 vs. 15 ± 3 m) may play a role as well. Hydrogeologic conditions that favor high levels of fecal contamination but low levels of arsenic in shallow groundwater should be taken into account during arsenic mitigation throughout South and Southeast Asia.