Concentration of arsenic by selected vegetables cultivated in the Yamuna flood plains (YFP) of Delhi,India

High Arsenic (As) concentrations have been reported in superficial water in the Yamuna flood plains (YFP), Delhi, which is being extensively used for agriculture. The concentration of As in some common vegetables such as Solanum lycopersicum (tomato), Abelmoschus esculentus (lady’s finger), Solanum melongena (brinjal), Lagenaria siceraria (bottle gourd), Raphanus sativus (radish), Zea mays (corn), and Luffa acutangula (ridge gourd) has been studied in this work. The range of As concentrations (dry weight) varies from 0.6 to 2.52 mg/kg with the highest accumulation of 2.52 mg/kg in radish followed by tomato (2.36 mg/kg). The order of As concentration in the decreasing order is R. sativus > S. lycopersicum > Z. mays > L. acutangula > L. siceraria > S. melongena > A. esculentus. Thus, As accumulation is the highest in roots and the lowest in least juicy fruits. The daily dietary intake of As through the consumption of various vegetables was also calculated. Though the mean As concentration was the highest in radish (2.52 mg/kg) but the highest amount of As is being consumed through tomato (0.383 mg/day), which is nearly three times the World Health Organization’s provisional maximum tolerable daily intake limit of 0.126 mg/day for a 60 kg person. High concentration of As in vegetables grown in YFP, Delhi is being reported here. This high contamination is primarily due to the presence of As in irrigation water having its source from coal-based thermal power plants in the vicinity of the area. If not checked properly, it will pose a serious health risk to people living in these densely populated areas surrounding YFP.     

Sediment Yield Index mapping and prioritization of Madia subwatershed,Sagar District of Madhya Pradesh (India)

Assessment of soil loss through Sediment Yield Index (SYI) is important for watershed planning, prioritization, and development. In the absence of measured sediment data, SYI expressing the relative sediment yield from different basins work as a basis for grading another basin to adopt erosion control measures. An attempt was made to evaluate SYI in wider scale by using cost-effective tools like remote sensing and geographical information system (GIS). SYI was calculated for Madia subwatershed, which consists of 29 microwatersheds and located in Sagar District, Madhya Pradesh (M.P.) The IRS LISS III data and Shuttle Radar Topography Mission (SRTM) digital elevation models (DEM) of 90-m resolution were used to identify land use characteristics and geomorphometric analysis. Major land use was observed as agricultural land (24.7 %), water bodies (16.7 %), forest area (10.2 %), and settlement (21.3 %). In categorization, similar overall accuracy was observed for dense forest, barren land, settlement, and water bodies. The highest SYI with a value more than 20 was observed in microwatershed Mw6, Mw7, and Mw24, which comprises 33 % of the total watershed area. It gives the information about the watershed area that requires very high priority.     

Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon,FL

Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, Slope_RED and Slope_NIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.     

Ocean Model Simulation of Southern Indian Ocean Surface Currents

The dynamic importance of the Southern Indian Ocean (SIO) lies in the fact that it connects the three major world oceans: the Pacific, Atlantic, and Indian Oceans. Modeling study has been used to understand the circulation pattern of this very important region. Simulation of SIO (10°N–60°S and 30°E–120°E) is performed with z-coordinate Ocean General Circulation Model (OGCM) viz; MOM3.0 and the results have been compared with observed ship drift data. It is found that except near coastal boundaries and in equatorial region, the simulated current reproduce most well known current pattern such as Antarctic Circumpolar Current (ACC), South Equatorial Current (SEC) etc. and bears a resemblance to that of the observed data; however the magnitude of the surface current is weaker in model than the observed data, which may be due to deficiency in the forcing field and boundary condition and problem with observed data. The annual mean wind stress curl computed over the oceanic domain reveals about ACC and its similar importance. The way in which the ocean responds to the windstress and vertically integrated transport using model output is fascinating and rather good.     

Mineralogy,petrography and micro-chemical characteristics of enclaves of mylonitic BIFs within Sukinda ultramafic complex,Odisha

Elongated NE-SW trending bodies of iron-rich rock are exposed adjacent to pyroxenite dyke within Sukinda ultramafic complex, Odisha. Field study followed by optical and electron microscopy, XRD and EPMA investigation reveal the rocks to be fine grained, weathered, limonitised; containing quartz, magnetite, hematite/martite and goethite. The rock has suffered from deformation during intrusion of chromiferous magma. It rarely shows banding/lamination, but largely exhibits mylonitic fabric, resulting from magmatic intrusion. The stronger deformation is evident from sub-grain formation, deformed mineral grains; often with orientation, stretching (boudinage) and shortening (folding); presence of porphyroclasts, pull-apart structure, undulose extinction, dynamic recrystallisation etc. From the microstructure and mineral abundance, the rock is designated as “Mylonitic Magentite Quartzite” (MMQ).Enrichment of some elements like Ni, Mg, Cr in the magnetite phase of MMQ is attributed to solid state diffusion of these elements from chromiferous mafic magma during thermal metamorphism. This is determined from electron probe microanalysis of iron-rich phase in MMQ, which is found to contain 88-90 wt% of FeO^(t) with ~1%, NiO, ~1%, MgO and 0.1% Cr₂O₃ having around 3 mole% of trevorite; 4-6% of magnesioferrite; 0.15-0.3% of chromite; 86-87% of magnetite and 3-4% of wustite. Considering presence of wustite as temperature indicator, the temperature of magma envisaged to be around 950-1100°C.In a later period, the MMQ has undergone oxidation and lateritisation owing to its prolonged exposure. During this process, new minerals like hematite and goethite substituted magnetite, resulting leaching of iron (FeO: 62-68%) and magnesium (MgO: 0.1-0.35) and enrichment of chromium (Cr₂O₃:4-7%) and nickel (NiO: 1.6-2.3%). The silica (SiO₂: 4-5%), alumina (Al₂O₃:~1%) are contributed by kaolinite, formed during lateritisation.The field and laboratory studies confirm these iron-rich exposures to be enclaves of BIFs, banded magnetite quartzite (BMQ) in particular, within the Sukinda chromiferous ultramafic complex. Micro-structural features and microchemical composition of iron minerals in these exposures are interpreted as the influence of forceful ultramafic intrusion into the existing BMQ and effect of thermal metamorphism followed by oxidation, weathering/lateritisation.     

Chemistry of acid production in black coal mine washery wastes

Column leaching tests on black coal mine washery wastes were performed, to determine the chemistry of acid generation. Coal mine coarse rejects and tailings were subjected to wet and dry cycle dissolution and subsequently column leached. The rates of iron sulphide oxidation and carbonate mineral dissolution were determined based on the drainage chemistry. The kinetic data from column leach experiments are used to predict the time required to deplete the acid producing and acid consuming minerals in the mine wastes. The acid production in the mine rejects was found to depend upon iron chemistry, carbonate chemistry, diffusion of oxygen, and permeability. The chemistry of the drainage from two different coal mines is compared.     

Geology and tectonic implications of tourmaline bearing leucogranite of Bastipadu,Kurnool, Andhra Pradesh,India

Tourmaline bearing leucogranite occurs as a pluton with pegmatitic veins intruding the Archaean granodiorite in the Bastipadu area, Kurnool district of Andhra Pradesh. We present field and petrographic relations, mineral chemistry and geochemical data for the leucogranite. It is essentially a two-mica granite, composed of quartz, perthite, microcline, albite, tourmaline and muscovite along with minor biotite and titanite. The euhedral tourmalines are regularly distributed in the rock. The geochemical studies show that the leucogranite is calc-alkaline, peraluminous to metaluminous which formed in a syn-collisional to volcanic arc-related setting. It displays strong ‘S’ type signatures with high K/Na ratios, moderately fractionated light rare earth elements, relatively flat heavy rare earth elements with \(\hbox {[Ce/Yb]}_\mathrm{N} \le 27.8\) and a strong negative Eu anomaly. The geochemical characteristics indicate that the leucogranite melt might have been generated from partial melting of metasediments. Electron probe microanalyser data show the presence of alkali group tourmaline in leucogranite represented by schorl and dravite. Tourmaline compositions plot in the Li-poor granitoids and associated pegmatites and aplites and metapelites/metasammites fields. Partial melting of boron-enriched source rocks is linked with the development of tourmalines in the leucogranite.     

Correction to: A Size-Dependent Bonded-Particle Model for Transversely Isotropic Rock and its Application in Studying the Size Effect of Shale

Geotechnical and Geological Engineering -