Status of anthropogenically induced metal pollution in the Kanpur-Unnao industrial region of the Ganga Plain, India

 The Ganga Plain is one of the most densely populated regions and one of the largest groundwater repositories of the Earth. For several decades, the drainage basin of the Ganga Plain has been used for the disposal of domestic and industrial wastes which has adversely affected the quality of water, sediments and agricultural soils of the plain. The concentrations of Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sn, Zn and organic carbon were determined in river sediments and soils of the Ganga Plain in the Kanpur-Unnao industrial region in 1994 and 1995 (pre-monsoon period of April–May). High contents (maximum values) of C-org (12.0 wt. %), Cr (3.40 wt. %), Sn (1.92 wt. %), Zn (4000 mg/kg), Pb (646 mg/kg), Cu (408 mg/kg), Ni (502 mg/kg) and Cd (9.8 mg/kg) in sediments (<20 μm fraction); and C-org (5.9 wt. %), Cr (2.16 wt. %), Sn (1.21 wt %), Zn (975 mg/kg) and Ni (482 mg/kg) in soils (<20 μm) in the pre-monsoon period of 1994 were found. From 1994 to 1995 the contents of Fe and Sn in sediments increase whereas those of C-org, Cd, Cu, Ni and Zn decrease. Considering the analytical errors, Al, Co, Cr, Mn and Pb do not show any change in their concentrations. In soils, the contents of Cd, Fe and Sn increase whereas those of Ni decrease from 1994 to 1995. Aluminium, Co, Cr, Cu, Mn, Pb and Zn do not show any change in their concentrations from 1994 to 1995. About 90% of the contents of Cd, Cr and Sn; 50–75% of C-org, Cu and Zn; and 25% of Co, Ni and Pb in sediments are derived from the anthropogenic input in relation to the natural background values, whereas in soils this is the case for about 90% of Cr and Sn; about 75% of Cd; and about 25% of C-org, Cu, Ni and Zn. The sediments of the study area show enrichment factors of 23.6 for Cr, 14.7 for Cd, 12.2 for Sn, 3.6 for C-org, 3.2 for Zn, 2.6 for Cu and 1.6 for Ni. The soils are enriched with factors of 10.7 for Cr, 9.0 for Sn, 3.6 for Cd, 1.8 for Ni and 1.5 for Cu and Zn, respectively. Received: 3 March 1998 · Accepted: 15 June 1998     

Environmental pollution control through geo – botanical means – a case study of Manikpur block, Korba coalfield, M.P., India

 In the present paper, an environmental analysis of Manikpur area, Korba coalfield, Bilaspur, M.P., India is undertaken. The area lies in the Geological Survey of India Toposheet no. 64J/11 Latitude 82°42′54″–82°45′10″ North; Longitude 22°18′46″–22°19′46″ East. The paper deals with pollution and its control measures through the natural plants found in the vicinity of the coalmines, namely Mangifera indica, Eucalyptus spp., Cassia siamea, Delbergia sissoo, etc. The plants control the Suspended Particulate Matter (SPM), Nitrous Oxides (NOx), Sulphur Oxides (SOx) of the mines and mining site. Therefore, plantation in the mining site should be encouraged. Received: 12 May 1998 · Accepted: 6 October 1998     

Investigation into the Behaviour of a Support System and Roof Strata During Sublevel Caving of a Thick Coal Seam

Monitoring of strata control parameters and behaviour of a powered support was carried out during an experimental trial of a mechanized longwall sublevel caving face for exploitation of a 7.5m thick coal seam. Field observations indicated that the requirement of support density for underwinning of top coal by sublevel caving under intact strata is different from that for underwinning under broken and fractured rock mass. Analysis of the leg closure observations and support resistance variations during different mining cycles showed rapid increase of the setting load density of the support in relation to the yield load density. This resulted in a large amount of leg closure during mining cycles of the sub level caving face under broken rock strata. Due to operational constraints, the field observations could not provide enough information to visualize the behaviour of the overlying rock strata. Simulation of the field conditions was therefore performed on a physical model to bridge this gap of information. Results of laboratory investigations on the physical model are combined with those of the field to explain the critical behaviour of the support system during sublevel caving under broken rock strata.     

Grey wolf optimizer: a new strategy to invert geophysical data sets

There is no meta‐heuristic approach best suited for solving all optimization problems making this field of study highly active. This results in enhancing current approaches and proposing new meta‐heuristic algorithms. Out of all meta‐heuristic algorithms, swarm intelligence is preferred as it can preserve information about the search space over the course of iterations and usually has fewer tuning parameters. Grey Wolves, considered as apex predators, motivated us to simulate Grey Wolves in the optimization of geophysical data sets. The grey wolf optimizer is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behaviour of Grey Wolves. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolves. The three main phases of hunting, that is searching, encircling and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the grey wolf optimizer, we performed inversion on the total gradient of magnetic, gravity and self‐potential anomalies. The results have been compared with the particle swarm optimization technique. Global minimum for all the examples from grey wolf optimizer was obtained with seven wolves in a pack and 2000 iterations. Inversion was initially performed on thin dykes for noise‐free and noise‐corrupted (up to 20% random noise) synthetic data sets. The inversion on a single thin dyke was performed with a different search space. The results demonstrate that, compared with particle swarm optimization, the grey wolf optimizer is less sensitive to search space variations. Inversion of noise‐corrupted data shows that grey wolf optimizer has a better capability in handling noisy data as compared to particle swarm optimization. Practical applicability of the grey wolf optimizer has been demonstrated by adopting four profiles (i.e. surface magnetic, airborne magnetic, gravity and self‐potential) from the published literature. The grey wolf optimizer results show better data fit than the particle swarm optimizer results and match well with borehole data.     

Potassium and Residue Management Options to Enhance Productivity and Soil Quality in Zero Till Maize–Wheat Rotation

Crop residue burning and imbalanced use of chemical fertilizers in intensive cereal–cereal rotations are present ecological threats in any agro‐ecosystem of the world. Therefore, identification of best suitable agricultural practices can be a feasible option. The present experiment was initiated in 2013 and consisted of four residue levels (0, 2, 4, and 6 Mg ha^?1) and five potassium (K) levels (0, 50, 100, 150% recommended dose of K and 50%RDK+K solubilizing bacteria, KSB). Crop residue (CR) and K management significantly improve crop and soil quality associated parameters. Among the treatments, maximum increase in crop growth, physiological parameters, grain yield, quality aspects, and water productivity are recorded with the application of 4–6 Mg ha^?1 CR. Application of 50%RDK+KSB also significantly increases crop and soil related parameters. Soil quality indicators (bulk density, pH, electrical conductivity, and available micronutrients) do not vary significantly with CR and K management. Change in soil organic carbon status, soil enzymes, and potassium‐solubilizing bacterial count are significantly increased with 4–6 Mg ha^?1 CR and application of 50%RDK+KSB, and this is in accordance with correlation study carried out. Therefore, it is concluded that CR retention (4–6 Mg ha^?1) and reduction of inorganic K fertilizer by 50% and inoculation of KSB enhance the soil quality indicators and thereby improve crop growth, physiological parameters, grain yield, and quality aspects along with water productivity under zero till maize–wheat rotation.     

Stochastic modelling of relative water permeability in vegetative soils with implications on stability of bioengineered slope

Vegetation is known to influence the hydrological state variables, suction \( \left( \psi \right) \) and volumetric water content (\( \theta_{w} \)) of soil. In addition, vegetation induces heterogeneity in the soil porous structure and consequently the relative permeability (\( k_{r} \)) of water under unsaturated conditions. The indirect method of utilising the soil water characteristic curve (SWCC) is commonly adopted for the determination of \( k_{r} \). In such cases, it is essential to address the stochastic behaviour of SWCC, in order to conduct a robust analysis on the \( k_{r} \) of vegetative cover. The main aim of this study is to address the uncertainties associated with \( k_{r} \), using probabilistic analysis, for vegetative covers (i.e., grass and tree species) with bare cover as control treatment. We propose two approaches to accomplish the aforesaid objective. The univariate suction approach predicts the probability distribution functions of \( {\text{k}}_{\text{r}} \), on the basis of identified best probability distribution of suction. The bivariate suction and water content approach deals with the bivariate modelling of the water content and suction (SWCC), in order to capture the randomness in the permeability curves, due to presence of vegetation. For this purpose, the dependence structure of \( \psi \) and \( \theta_{w} \) is established via copula theory, and the \( k_{r} \) curves are predicted with respect to varying levels of \( \psi - \theta_{w} \) correlation. The results showed that the \( k_{r} \) of vegetative covers is substantially lower than that in bare covers. The reduction in \( k_{r} \) with drying is more in tree cover than grassed cover, since tree roots induce higher levels of suction. Moreover, the air entry value of the soil depends on the magnitude of \( \psi - \theta_{w} \) correlation, which in turn, is influenced by the type of vegetation in the soil. \( k_{r} \) is found to be highly uncertain in the desaturation zone of the relative permeability curve. The stochastic behaviour of \( k_{r} \) is found to be most significant in tree covers. Finally, a simplified case study is also presented in order to demonstrate the impact of the uncertainty in \( k_{r} \), on the stability of vegetates slopes. With an increment in the parameter \( \alpha \), factor of safety (FS) is found to decrease. The trend of FS is reverse of this with parameter \( n \). Overall FS is found to vary around 4–5%, for both bare and vegetative slopes.     

Dynamic stress concentration in pre-stressed poroelastic media due to moving punch influenced by shear wave

During the occurrence of earthquake, the shear wave propagates in the rocks present inside/at the Earth’s crust. The propagation of shear wave may lead to the progression of punch present inside the rock medium. As a result of this, substantial stress accumulated at the vicinity of propagating punch inside rock medium which significantly affects the stability of various geological and human-made structure and, hence, may cause failure of structure. Therefore, the analysis of stress concentration at the vicinity of punch moving due to shear wave propagation has become prominent in the area of seismology. In the present paper, an analytical perspective has been employed to discuss the influence of velocity of moving punch associated with the propagation of shear wave on developed dynamic stress concentration (DSC) in three types of pre-stressed vertical transversely isotropic (VTI) poroelastic media viz. granite (an igneous rock); sandstone (a sedimentary rock); and marble (a metamorphic rock). The closed form expression of DSC for the force of constant intensity has been derived with the aid of Weiner-Hopf technique along with Galilean and two-sided Fourier integral transformations. The noticeable influence of different affecting parameters (viz. velocity of moving punch associated with the shear wave propagation, horizontal compressive/tensile initial stresses, vertical compressive/tensile initial stress, porosity, and anisotropy parameter) on dynamic stress concentration has also been reported. Numerical computation and graphical illustrations have been carried out for the aforementioned three different types of porous rocks to investigate the profound impact of affecting parameters on DSC. Moreover, some noteworthy peculiarities have also been derived from the obtained expression of dynamic stress concentration.     

Application of minimum relative entropy theory for streamflow forecasting

This paper develops and applies the minimum relative entropy (MRE) theory with spectral power as a random variable for streamflow forecasting. The MRE theory consists of (1) hypothesizing a prior probability distribution for the random variable, (2) determining the spectral power distribution, (3) extending the autocorrelation function, and (4) doing forecasting. The MRE theory was verified using streamflow data from the Mississippi River watershed. The exponential distribution was chosen as a prior probability in applying the MRE theory by evaluating the historical data of the Mississippi River. If no prior information is given, the MRE theory is equivalent to the Burg entropy (BE) theory. The spectral density obtained by the MRE theory led to higher resolution than did the BE theory. The MRE theory did not miss the largest peak at 1/12th frequency, which is the main periodicity of streamflow of the Mississippi River, but the BE theory sometimes did. The MRE theory was found to be capable of forecasting monthly streamflow with a lead time from 12 to 48 months. The coefficient of determination (r ²) between observed and forecasted stream flows was 0.912 for Upper Mississippi River and was 0.855 for Lower Mississippi River. Both MRE and BE theories were generally more reliable and had longer forecasting lead times than the autoregressive (AR) method. The forecasting lead time for MRE and BE could be as long as 48–60 months, while it was less than 48 months for the AR method. However, BE was comparable to MRE only when observations fitted the AR process well. The MRE theory provided more reliable forecasts than did the BE theory, and the advantage of using MRE is more significant for downstream flows with irregular flow patterns or where the periodicity information is limited. The reliability of monthly streamflow forecasting was the highest for MRE, followed by BE followed by AR.     

Hydrologic model-based Palmer indices for drought characterization in the Yellow River basin,China

The Palmer indices (PIs) that have been most widely used for drought monitoring and assessment are criticized for two main drawbacks: coarse hydrological accounting processes with a simplified two-stage bucket soil water balance model and arbitrary rules for defining drought properties and standardizing index values through limited calibration and comparison. In this study, we introduce a new proposal of the VIC hydrologic model-based Palmer drought scheme, where traditional PIs (e.g. PDSI) can readily be calculated on the basis of distributed finescale hydrologic simulations. Moreover, recent variants of PI (i.e., SPDI and SPDI-JDI) also provide a preferable standardization strategy that allows probabilistic invariability and better spatio-temporal comparability of computed drought indices. Using gridded meteorological forcing, soil and vegetation data to drive the three-layer VIC model, both non-VIC and VIC-based PIs are investigated to examine their performances for drought characterization and detection. Results indicate that VIC hydrologic model would allow for adjustments in statistical properties of computed PDSI and VIC-based SPDI is also preferable to PDSI for better statistical robustness and spatio-temporal consistency/comparability. Moreover, the joint SPDI-JDI has the strength of integrating multi-scale probabilistic properties and drought information released by individual SPDI, providing overall drought conditions that take into account the onset, persistence and termination of droughts. At proposed 0.25° grid scale, the VIC-based SPDI-JDI indicates high frequency and long total time of drought condition in the Yellow River basin (YRB), China. Although no significant temporal trends are found in identified drought duration and severity, both the seasonal and annual drought index values demonstrate a downward trend (higher drought intensity) for considerable proportions of the YRB. These findings imply high drought risk and potential drying stress for this region. The new framework of hydrologic model-based PIs can help to strengthen our knowledge and/or practices in regional drought monitoring and assessment.