Impact of termite activity on soil environment: A perspective from their soluble chemical components

An investigation on varied types of termite mounds relative to the nearby soils that are not inhabited by the termites in different places of Cameroon show that the activity of the termites is increasing the contents of most major and some trace elements in the termite mounds, except for Si and sometimes Fe, Mn, Na and K. These released elements are relocated into newly formed mineral phases that are dissolved by either H₂O or dilute HCl leachings. The Ca and Mn released by the termite activity testify for crystallization of Ca-Mg carbonates and phosphates as well as of Fe oxy-hydroxides and/or Mn hydroxides. Termite activity also induces an increase in the lanthanide contents, the mound materials being especially enriched in light lanthanides relative to the corresponding soils without termite activity. The shapes of the patterns support precipitation of Mn-Fe oxy-hydroxides and Ca carbonates-phosphates. The increased amounts of Eu and Ce linked to termite activity seem to relate to the occurrence of reducing agents that are released by the termites, modifying Eu⁺³ into Eu⁺² and Ce⁺⁴ into Ce⁺³, favoring in turn selective incorporation of Eu⁺² and Ce³⁺ in the new phases of the termite mounds. Another consequence of the termite activity is the precipitation of H₂O and HCl extractable phases having low Sr/Ca ratios. Even if the K/Rb values of the termite mounds are typical for common soil-forming silicate minerals, their relocation by an inorganic process alone does not explain an abnormally high ratio in the H₂O leachable mineral phases. It was also shown that the main source for K and Rb of the dissolved phases is not only the interlayer site of clay particles, but also nutrients immobilized in and by the termites.     

A new approach using polyvinylidene fluoride immobilised calf-intestinal alkaline phosphatase for uranium bioprecipitation

Uranium is a predominant radionuclide arising out of operations of mining, milling and fuel fabrication for nuclear power plants. While chemical and biological methods using several micro-organisms have been employed for the remediation of uranium from such effluents, use of immobilised enzymes for the purpose has not been reported. The current investigation deals with a purified calf-intestinal alkaline phosphatase which is capable of driving uranium precipitation in the presence of a phosphate-containing substrate. The enzyme, both in free form and in an immobilised form on polyvinylidene fluoride membrane, was investigated for the precipitation of uranium using para-nitrophenyl phosphate as substrate. The results indicated that while the enzyme in free form could precipitate most of the uranium within 6 h, the immobilised enzyme could achieve the same in 24 h. There was a spurt in the precipitation of uranium by the immobilised enzyme during the initial period of 3 h followed by a linear increase for the remaining period. The immobilised enzyme was found to be stable and catalytically active for 3 years in precipitating uranium. The results obtained offer polyvinylidene fluoride immobilised alkaline phosphatase from calf intestine as a new tool for the remediation of uranium from effluents. Further, evidence has been also shown that no requirement for any cellular architecture/scaffolding exists for uranium mineralisation to occur.     

Shelf water entrainment by Gulf Stream warm-core rings between 75°W and 50°W during 1978–1999

Previous work concerning Gulf Stream warm-core rings (WCRs) and their associated shelf water entrainments have been based upon single surveys or time series from individual WCRs. To date, estimates of annual shelf water volume entrained into the Slope Sea by WCRs and its interannual variability have not been made. Using a long time series of satellite-derived sea surface temperature (SST) observations of Slope Sea WCRs, we have completed an analysis of 22 years of WCR data (1978–1999) between 75°W and 50°W to understand the interannual variability of WCRs and their role in entraining shelf water. Satellite-derived SST data digitized at Bedford Institute of Oceanography are analyzed using an ellipse-fitting feature model to determine key WCR characteristics including WCR center position, radius and orientation. Key characteristics are then used to compute WCR swirl velocity by finite-differencing WCR orientations (θ) obtained from the feature model time series. Global mean WCR-edge swirl velocity calculated from all observations is 105.72±10.7 km day⁻¹ (122.36±12.4 cm s⁻¹), and global mean WCR radius is 64.8±6.2 km. Primary and derived WCR data are incorporated into a two-dimensional ring entrainment model (RM) using the quasi-geostrophic approximation of the potential vorticity equation. The RM defines ambient water as entrained by a WCR only if the gradient of relative vorticity term (horizontal shear) dominates the potential vorticity. Proximity of a WCR to the position of the shelf-slope front (SSF) is then used to determine whether the ambient water is entrained from the outer continental shelf. WCR-induced shelf entrainment derived from the RM displays considerable spatial variability, with maximum entrainment occurring offshore of Georges Bank, advecting a mean total annual shelf water volume of 7500 km³ year⁻¹ from the region. Estimates of shelf water fluxes display significant interannual variability, which may be in part due to the observed covariance between WCR occurrences and the state of the North Atlantic Oscillation (NAO). Increased (decreased) occurrences of WCRs are evidenced during positive (negative) phases of the NAO. The total mean annual shelf-wide WCR-induced shelf water transport is estimated to be 23,700 km³ year⁻¹ (0.75 Sv), accounting for nearly 25% of the total transport in the Slope Sea region neighboring the outer continental shelf.     

Generalized Potential Temperature in a Diagnostic Study of High Impact Weather over an Urban Station of India

The tropospheric atmosphere is neither absolutely dry nor completely saturated. It is, in general, moist. The purpose of the present study is to reveal the role of generalized potential temperature (GPT) in describing the humid state of the real moist atmosphere pertaining to understanding the prevalence of high impact weather systems over an urban station, Kolkata (22°32′N; 88°20′E), of India. A comparative study among GPT, equivalent potential temperature (EPT), potential temperature and relative humidity to reveal the significance of GPT in a precise understanding of the high impact weather of Kolkata is carried out. To attain the objectives, 50 cases of thunderstorms, 15 cases of tropical cyclones and 15 heavy rainfall days are selected during the pre-monsoon season (April–May) over Kolkata (22°32′N; 88°20′E), India. The condition—decision support system of rough set theory is adopted as the methodology. The result of the study reveals that GPT is the most pertinent convective parameter in estimating the prevalence of the high impact weather of Kolkata during the pre-monsoon season and is observed to be better than RH. The results, thus, show that the moist air is capable of describing the distribution of water vapour and thermodynamic properties of the real atmosphere more precisely than an absolutely dry and completely saturated state of the atmosphere.     

Empirical approaches in prediction of reservoir sediment distribution——An experience of 57 reservoirs in the USA and India

An accurate prediction of sediment distribution may minimize economic losses through proper and timely planning of the functional activities of a reservoir.This study assesses different temporal and spatial factors that affect for sediment deposition in a reservoir and its distribution.This study also focuses on evaluation of two popular distribution prediction methodologies,Area Increment and Empirical Area Reduction,based on experience with sediment distribution in 57 reservoirs in the USA and India.A non-iterative processed empirical distribution model(NPEDM) and a linear regression trend model(LRTM) are proposed to predict sediment distribution.Silt contributing area and inflow entering a reservoir are found to be the most significant factors affecting in reservoir sediment deposition.Compared to the Empirical Area Reduction method,the Area Increment method provided better prediction.The reservoir classification approach and empirical design distribution type curves given by Borland and Miller(1960) are found to be rational.Shape factor values for different periods indicate that reservoir shape(type) changes with time.Thus,long term prediction is not desirable in Type-Ⅱ Ⅲ reservoirs using the Empirical Area Reduction method.Newly developed the NPEDM shows reasonably good prediction of sediment distribution.The NPEDM is very easy to apply and can be used in any reservoir of any size.Extrapolation of the trend of sediment distribution obtained from the LRTM indicates an accurate short term prediction in a few reservoirs as causes of temporal and spatial variations of sediment distribution including the factors of uncertainties of sediment deposition are implicit within the methodology.     

Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach

Natural Hazards - The endeavor of the present research is to nowcast the spatial visibility during fog over the airport of Kolkata (22.6°N; 88.4°E), India, with artificial neural network...     

Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)

Natural Hazards - Forecasting, with precision, the location of landfall and the height of surge of cyclonic storms prevailing over any ocean basin is very important to cope with the associated...     

Effect of suspended sediment concentration on local scour around cylinder for clay–sand mixed sediment beds

Suspended sediments present in the flow are known to affect the flow resistance, velocity distribution and turbulent characteristics. Experiments were conducted in the laboratory flume to see the effect of suspended sediment concentration (SSC) on local scour around a cylindrical pier for a wide range of clay–sand mixed sediment beds for SSC up to 2700 mg/L. It has been observed that the effect of SSC on equilibrium scour hole parameters such as maximum equilibrium scour depth, and longitudinal and transverse extent of scour hole can be significant. Present data showed that the presence of SSC in the range 993–1332 mg/L can increase maximum equilibrium scour depth as much as 1.54 times compared to the clear water case. However, tests made for SSC in the range 2456–2700 mg/L showed that the maximum equilibrium scour depth reduced compared to that for SSC in the range 993–1332 mg/L, but these maximum equilibrium scour depths were still larger than that obtained for clear water. The effect of SSC on time variation of scour and equilibrium scour hole geometry was further investigated.     

Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April?CMay) over Kolkata (22°32??N, 88°20??E), India. The pre-monsoon thunderstorms during 1997?C2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12?h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.