Characterization of Black Carbon in the Ambient Air of Agra,India:Seasonal Variation and Meteorological Influence

This study characterizes the black carbon in Agra, India home to the Taj Mahal—and situated in the Indo-Gangetic basin.The mean black carbon concentration is 9.5 μg m~(-3)and, owing to excessive biomass/fossil fuel combustion and automobile emissions, the concentration varies considerably. Seasonally, the black carbon mass concentration is highest in winter, probably due to the increased fossil fuel consumption for heating and cooking, apart from a low boundary layer. The nocturnal peak rises prominently in winter, when the use of domestic heating is excessive. Meanwhile, the concentration is lowest during the monsoon season because of the turbulent atmospheric conditions and the process of washout by precipitation. The ratio of black carbon to brown carbon is less than unity during the entire study period, except in winter(December). This may be because that biomass combustion and diesel exhaust are major black carbon contributors in this region, while a higher ratio in winter may be due to the increased consumption of fossil fuel and wood for heating purposes. ANOVA reveals significant monthly variation in the concentration of black carbon; plus, it is negatively correlated with wind speed and temperature. A high black carbon mass concentration is observed at moderate(1–2 m s~(-1)) wind speed, as compared to calm or turbulent atmospheric conditions.     

Homogenization of Earthquake Catalog for Northeast India and Adjoining Region

A catalog for northeast India and the adjoining region for the period 1897–2009 with 4,497 earthquakes events is compiled for homogenization to moment magnitude M _w,GCMT in the magnitude range 3–8.7. Relations for conversion of m _b and M _s magnitudes to M _w,GCMT are derived using three different methods, namely, linear standard regression, inverted standard regression (ISR) and orthogonal standard regression (OSR), for different magnitude ranges based on events data for the catalog period 1976–2006. The OSR relations for M _s to M _w,GCMT conversion derived in this paper have significantly lower errors in regression parameters compared to the relations reported in other studies. Since the number of events with magnitude ≥7 for this region is scanty, we, therefore, considered whole India region to obtain the regression relationships between M _w,GCMT and M _s,ISC. A relationship between M _w,GCMT and M _w,NEIC is also obtained based on 17 events for the range 5.2 ≤ magnitude ≤ 6.6. A unified homogeneous catalog prepared using the conversion relations derived in this paper can serve as a reference catalog for seismic hazard assessment studies in northeast India and the adjoining region.     

Isolated Archosaur teeth from the green sandstone capping the Coralline Limestone (Bagh Group) of the Narmada valley: Evidence for the presence of pre‐Late to Late Maastrichtian abelisaurids in India

Recent field prospecting in the Cretaceous sequences of the lower Narmada valley has led to the discovery of three isolated archosaur teeth from the upper part of marine Cretaceous rocks of the Bagh Group. The specimens were recovered by surface prospecting from an oyster‐bearing green sandstone bed occurring at the top of the Coralline Limestone (Coniacian) from a site near Phutibawri village, Dhar District, Madhya Pradesh, India. Of the three teeth recovered from this horizon, two are identified with abelisaurid dinosaurs and the third one with an indeterminate crocodile. The abelisaurid teeth conform to the premaxillary and maxillary tooth morphology of Majungasaurus and Indosuchus. Earlier reports of abelisaurid dinosaurs from India are from the Upper Cretaceous (Maastrichtian) Lameta Group of Jabalpur, Pisdura (Central India) and Balasinor (Western India) and Upper Cretaceous (Late Maastrichtian) Kallamedu Formation (South India). As no associated age diagnostic fossils are found, the specimens described here are considered to represent pre‐Late to Late Maastrichtian age based on the known ages of the underlying and overlying formations. The new finds, therefore, document stratigraphically the oldest occurrence of abelisaurid dinosaurs known from the Indian subcontinent.     

Evaluation of Composts from Agricultural Wastes with Fish Pond Sediment as Bulking Agent to Improve Compost Quality

Composts with five different ratios of agricultural wastes, viz. rice straw (RS), wheat straw (WS), potato plant (PP), and mustard stover (MS) were prepared with or without fish pond bottom sediment to investigate the compost maturity and their suitability for field application. The composting process was monitored through the changes in physico‐chemical parameters and germination index (GI) at every 7 days interval of the composting process. All the composts were dark brown and smelled like forest soil within 56 days of composting, which reflected its matured status. On the basis of the physico‐chemical parameters (bulk density: 0.84 g/cm³; pH 7.05; electrical conductivity: 3.52 mS/cm; cation exchange capacity:82.4 cmol/kg; total carbon:321.4 g/kg; total nitrogen: 16.9 g/kg; As: 6.8 mg/kg; Cd: 2.96 mg/kg; Cr: 29.6 mg/kg, Cu: 243.6 mg/kg; Hg: 0.019 mg/kg; Ni: 24.3 mg/kg; Pb: 62.1 mg/kg and Zn: 812 mg/kg) and GI (89–96%), it could be concluded that RS/WS/PP/MS, 1:1:2:1 v/v/v/v with fish pond sediment produced better compost in accordance with the Indian compost standard. Application of a combined randomized block design analysis revealed that there is a significant difference in the responses of the five composts, in relation to the time of composting. Hierarchical clustering algorithm was applied with a view to form homogeneous groups of five different composts on the basis of different physico‐chemical parameters. Therefore, the ratio of waste incorporation is an important decision for composting and addition of pond sediment can improve the quality of compost.     

Cobalt‐Bearing Grunerite in the Metamorphosed Banded Iron Formation in Orissa,India

Banded iron formation (BIF) comprising high grade iron ore are exposed in Gorumahisani‐Sulaipat‐Badampahar belt in the east of North Orissa Craton, India. The ores are multiply deformed and metamorphosed to amphibolite facies. The mineral assemblage in the BIF comprises grunerite, magnetite/martite/goethite and quartz. Relict carbonate phases are sometimes noticed within thick iron mesobands. Grunerite crystals exhibit needles to fibrous lamellae and platy form or often sheaf‐like aggregates in linear and radial arrangement. Accicular grunerite also occur within intergranular space of magnetite/martite. Grunerite needles/accicules show higher reflectivity in chert mesoband and matching reflectance with that of adjacent magnetite/martite in iron mesoband. Some grunerite lamellae sinter into micron size magnetite platelets. This grunerite has high ferrous oxide and cobalt oxide content but is low in Mg‐ and Mn‐oxide compared to the ones, reported from BIFs, of Western Australia, Nigeria, France, USA and Quebec. The protolith of this BIF is considered to be carbonate containing sediments, with high concentrations of Fe and Si but lower contents of cobalt and chromium ± Mg, Mn and Ni. During submarine weathering quartz, sheet silicate (greenalite) and Fe‐Co‐Cr (Mg‐Mn‐Ni)‐carbonate solid solution were formed. At the outset of the regional metamorphic episode grunerite, euhedral magnetite and recrystalized quartz were developed. Magnetite was grown at the expense of carbonate and later martitized under post‐metamorphic conditions. With the increasing grade of metamorphism greenalite transformed to grunerite.     

Modeling fractionally integrated maximum temperature series in India in presence of structural break

In this study, the long memory behaviour of monthly maximum temperature of India for the period 1901 to 2007 is investigated. The correlogram of the series reveals a slow hyperbolic decay, a typical shape for time series having the long memory property. Wavelet transformation is applied to decompose the temperature series into time–frequency domain in order to study the local as well as global variation over different scale and time epochs. Significant increasing trend is found in the maximum temperature series in India. The rate of increase in maximum temperature accelerated after 1960s as compared to the earlier period. Here, an attempt is also made to detect the structural break for seasonally adjusted monthly maximum temperature series. It is found that there is a significant break in maximum temperature during July, 1963. Two-stage forecasting (TSF) approach to deal with the coexistence of long memory and structural change in temperature pattern is discussed thoroughly. The forecast performance of the fitted model is assessed on the basis of relative mean absolute prediction error (RMAPE), sum of squared errors (SSE) and mean squared errors (MSE) for different forecast horizons.     

Blast Vibration and Fragmentation Control at Heavily Jointed Limestone Mine

Geotechnical and Geological Engineering - The presence of structural inhomogeneity in a rock mass in the form of joints, fractures, bedding planes is a very common phenomenon. These discontinuities...     

A note on the lithosphere thickness and heat flow density of the Indian Craton from MAGSAT data

The Curie depth map of India compiled from MAGSAT data has been used for preparing the lithosphere thickness and the surface heat flow density maps of the Indian Craton, utilizing the concept of magnetothermometry. The lithosphere thicknesses of the major Indian geological units/provinces, as obtained from the prepared map, are found to be in reasonably good agreement with the previously published values for these regions. Also, the surface heat flow density values obtained from the prepared maps closely follow the previously published results. The maps are useful in providing first order estimates of lithosphere thickness and surface heat flow density of the important geological units/provinces of India.     

Wavelet-based combination approach for modeling sub-divisional rainfall in India

Agriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.