Toxic metals in Venice lagoon sediments: model,observation, and possible removal

We have modeled the distribution of nine toxic metals in the surface sediments from 163 stations in the Venice lagoon using published data. Three entrances from the Adriatic Sea control the circulation in the lagoon and divide it into three basins. We assume, for purposes of modeling, that Porto Marghera at the head of the Industrial Zone area is the single source of toxic metals in the Venice lagoon. In a standing body of lagoon water, concentration of pollutants at distancex from the source (C ₀) may be given byC=C ₀e^–kx wherek is the rate constant of dispersal. We calculatedk empirically using concentrations at the source, and those farthest from it, that is the end points of the lagoon. Averagek values (ppm/km) in the lagoon are: Zn 0.165, Cd 0.116, Hg 0.110, Cu 0.105, Co 0.072, Pb 0.058, Ni 0.008, Cr (0.011) and Fe (0.018 percent/km), and they have complex distributions. Given thek values, concentration at source (C ₀), and the distancex of any point in the lagoon from the source, we have calculated the model concentrations of the nine metals at each sampling station. Tides, currents, floor morphology, additional sources, and continued dumping perturb model distributions causing anomalies (observed minus model concentrations). Positive anomalies are found near the source, where continued dumping perturbs initial boundary conditions, and in areas of sluggish circulation. Negative anomalies are found in areas with strong currents that may flush sediments out of the lagoon. We have thus identified areas in the lagoon where higher rate of sediment removal and exchange may lessen pollution.     

Noble gases in the earth's mantle

The abundances and isotopic compositions of noble gases in two samples from ultramafic xenoliths in alkali basalt, a young kaersutitic amphibole separated from a peridotite xenolith from Dish Hill, California and an ancient whole-rock lherzolite xenolith from Baja California, are reported and compared with the results of analyses on other mantle samples. In addition to previously recognized excesses of ³He and ¹²⁹Xe, our results indicate that ambient gases in the mantle show a general enrichment of the lighter-mass nonradiogenic isotopes of Ar, Kr and Xe, and Ar with ⁴⁰Ar/³⁶Ar = 3 · 10².     

On disturbances in a viscoelastic (three-parameter type) medium acted upon by a magnetic field

Summary In this note, the problem of disturbances has been investigated in two perfectly (electrically) conductive media-an ideal liquid and a viscoelastic solid of three-parameter type-acted upon by a magnetic field. The electromagnetic equations of Maxwell and the equations of mechanical motion together with the stress-strain relation of the viscoelastic material of three-parameter type have been used to work out the problem. The results exhibit the predominance of transient behaviour in the displacements as well as in the stresses.     

Absorption line redshift distribution of QSOs

A gap in a distribution is the interval between two consecutive values. Analysis of gaps in the absorption line redshift distribution (241 values) of QSOs shows a definite trend in the distribution of gaps. The trend indicates that the absorption line distribution is not random, but does not suggest any periodicity.     

Self-regulated gravitational accretion in protostellar discs

We present a numerical model for the evolution of a protostellar disc that has formed self-consistently from the collapse of a molecular cloud core. The global evolution of the disc is followed for several million years after its formation. The capture of a wide range of spatial and temporal scales is made possible by use of the thin-disc approximation. We focus on the role of gravitational torques in transporting mass inward and angular momentum outward during different evolutionary phases of a protostellar disc with disc-to-star mass ratio of order 0.1. In the early phase, when the infall of matter from the surrounding envelope is substantial, mass is transported inward by the gravitational torques from spiral arms that are a manifestation of the envelope-induced gravitational instability in the disc. In the late phase, when the gas reservoir of the envelope is depleted, the distinct spiral structure is replaced by ongoing irregular non-axisymmetric density perturbations. The amplitude of these density perturbations decreases with time, though this process is moderated by swing amplification aided by the existence of the disc's sharp outer edge. Our global modelling of the protostellar disc reveals that there is typically a residual non-zero gravitational torque from these density perturbations, i.e. their effects do not exactly cancel out in each region. In particular, the net gravitational torque in the inner disc tends to be negative during first several million years of the evolution, while the outer disc has a net positive gravitational torque. Our global model of a self-consistently formed disc shows that it is also self-regulated in the late phase, so that it is near the Toomre stability limit, with a near-uniform Toomre parameter Q ≈ 1.5–2.0. Since the disc also has near-Keplerian rotation, and comparatively weak temperature variation, it maintains a near-power-law surface density profile proportional to r ^−3/2.     

Comparison of dissolved and particulate arsenic distributions in shallow aquifers of Chakdaha, India, and Araihazar, Bangladesh

Background  

The origin of the spatial variability of dissolved As concentrations in shallow aquifers of the Bengal Basin remains poorly understood. To address this, we compare here transects of simultaneously-collected groundwater and aquifer solids perpendicular to the banks of the Hooghly River in Chakdaha, India, and the Old Brahmaputra River in Araihazar, Bangladesh.     

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.     

Impact of Satellite Altimetry on Simulations of Sea Level Variability by an Indian Ocean Model

This article describes the impact of satellite altimeter data on the simulations of sea level variability (SLV) by a nonlinear reduced gravity model of the entire Indian Ocean. The model has been forced by 6-hourly analyzed wind stress data containing SSM/I observations and has been able to produce realistic circulation features. However, SLV values observed by Topex/Poseidon altimeter do not fit these simulations because of imperfect initial data. Hence an attempt has been made to initialize the model using altimeter data. The initialized model-generated SLVvalues have been compared with SLV derived by altimeter for monsoon as well as nonmonsoon months of 1996. Experimental runs have been performed for 10 days, 20 days, and one month. It has been found that the initialized model results on the final day of these experiments are in very good agreement with altimeter data of the same day. It is thus possible, in principle, to hindcast and forecast sea level variations in the time scale of 10 days to one month with the availability of good quality wind data for forcing the model and altimeter observations of sea level for initializing it.     

A multivariate statistical study of copper mineralization in the central section of Mosaboni Mine, Eastern Singhbhum, India

A multivariate statistical analysis was carried out with log-transformed values of Cu, Ni, Co, Pb, Zn, Ag, Cr, Mn, Ca, and Sr in several sets of samples collected across the mineralized base metal zone in sheared soda granite, feldspathic schist, and chlorite schist from the central section of Mosaboni Mine of the famous Singhbhum Copper Belt of eastern India. Linear correlation coefficient matrices of two sets of ore samples (>0.5% Cu)—one from levels 18 and 21 and the other from levels 25 and 28—indicate two well-defined and distinct clusters comprising Cu, Ni, Co, Pb, and Zn on one hand and Ca, Sr, and Mn on the other. Varimax-rotatedR-mode factor analysis of two above-noted sample sets, taken along with available geologic information, indicates that over 80% of the variability in data matrices for 9–10 elements can be accounted for by four distinct processes: (a) an early phase of copper mineralization which apparently replaced Mn, Ca, and Sr in the host rock; (b) a silicate-cum-oxide phase of crystallization/recrystallization of host rock; (c) remobilization of sulfide-forming ore elements (Cu, Ni, Co, Pb, and Zn); and (d) a phase of mineralization of Ag which appears to have replaced Cr, Ca and Cu. Process (c) was quantitatively most important. Factor score studies are suggestive of preferred introduction of Ni, Co, Pb, and Zn along central parts of preexisting copper-mineralized zones.