Evaluation of geotechnical properties of overburden dump for better reclamation success in mining areas

Coal-mining activities are precursors to land degradation, a part of which is directly related to the generation of huge quantity of overburden (OB) material. The problem of managing OB is mitigated through reclamation, which is directly associated with the geotechnical properties of these dumps. The main objective of this study was to assess the potential of an alternative management plan of OB by analyzing physical properties of these dumps. This alternative management plan of OB could enhance reclamation success by fostering conditions conducive for vegetation growth. This is especially required in old mining areas, where salvaging of topsoil was not done, and as a result, reclamation was affected. For the present study, 12 representative OB samples were collected from two open-cast coal mines (OCP-A and OCP-B) of Bastacolla area in Jharia Coalfield, Dhanbad, India. The samples were analyzed for different geotechnical properties. The results revealed that optimization of these parameters could help in enhancing the reclamation success of the area. From land-use classification, it was observed that about 242 ha of the study area was OB dump; if these areas are reclaimed successfully, then they can act as a potential source of carbon sequestration, and furthermore, they will improve the esthetics and the overall environmental quality at these degraded areas.     

Radon as seismic precursor: new data with well water of Jalpaiguri,India

This paper reports the measurement of radon concentration in well water at the site of Jalpaiguri (26°32′N, 88°46′E) near the active fault zone of West Bengal, India. Radon concentration has been measured in well water with the help of solid-state nuclear track detectors (SSNTD). The study indicates a positive correlation between radon anomaly and earthquake. The data of radon content in well water have been compared with that in soil gas at the same site.     

Electrokinetic retention, migration and remediation of nitrates in silty loam soil under hydraulic gradients

Contamination of groundwater by nitrates leaching from intensive agricultural and livestock operations have become a major concern for surrounding communities that use groundwater as their water supply. High levels of nitrate in drinking water poses a significant risk to human health, i.e., methaemoglobinaemia (“blue baby” syndrome).

The traditional pump-and-treat method is ineffective in medium to fine-textured agricultural soils due to the low hydraulic conductivity. This paper presents the results of a laboratory experiment investigating the feasibility of using electrokinetic treatment in retaining, accumulating, moving and remediating nitrates in a silty loam soil under hydraulic gradients.

A hydraulic gradient of 1.25 was applied to the horizontal soil columns to simulate a groundwater movement system. The study was performed in two stages. During the first stage of the experiment, the anode located at the inflow end of the columns was able to retard the movement of nitrates even under a hydraulic gradient of 1.25. After 15 days of flow, the effluent nitrate concentration in the control column rose to 90 mg l⁻¹ while no nitrates were detected in the effluent from columns subjected to the electrokinetic treatment.

After 15 days, the polarity of the electrodes was switched and this second stage lasted another 20 days. The cathode near the inflow end promoted the conversion of nitrates entering the column to other forms. The anode near the outflow end promoted the migration and accumulation of negatively charged nitrate ions towards the outflow end. By the 12th day, the nitrate concentrations in the electrokinetically treated columns were brought down to <5 mg NO₃-N l⁻¹. Electrokinetic treatment retarded nitrate movement against a hydraulic gradient of 1.25 and effectively restored a medium-textured soil contaminated with NO₃-N.

The NO₂-N level remained below 1 mg l⁻¹ throughout the experiment. The hydraulic conductivity varied between 1.0E–7 and 3.6E–7 m s⁻¹. The current requirement varied between 3 and 6 mA.     

Automated star–galaxy segregation using spectral and integrated band data for TAUVEX/ASTROSAT satellite data pipeline

We employ an Artificial Neural Network (ANN) based technique to develop a pipeline for automated segregation of stars from the galaxies to be observed by Tel-Aviv University Ultra-Violet Experiment (TAUVEX). We use synthetic spectra of stars from UVBLUE library and selected International Ultraviolet Explorer (IUE) low-resolution spectra for galaxies in the ultraviolet (UV) region from 1250 to 3220 Å as the training set and IUE low-resolution spectra for both the stars and the galaxies as the test set. All the data sets have been pre-processed to get band integrated fluxes so as to mimic the observations of the TAUVEX UV imager. We also perform the ANN based segregation scheme using the full length spectral features (which will also be useful for the ASTROSAT mission). Our results suggest that, in the case of the non-availability of full spectral features, the limited band integrated features can be used to segregate the two classes of objects; although the band data classification is less accurate than the full spectral data classification.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

Scanning sky monitor (SSM) onboard AstroSat

Scanning Sky Monitor (SSM) onboard AstroSat is an Xray sky monitor in the soft X-ray band designed with a large field of view to detect and locate transient X-ray sources and alert the astronomical community about interesting phenomena in the X-ray sky. SSM comprises position sensitive proportional counters with 1D coded mask for imaging. There are three detector units mounted on a platform capable of rotation which helps covering about 50% of the sky in one full rotation. This paper discusses the elaborate details of the instrument and few immediate results from the instrument after launch.     

First Observations from the Multi-Application Solar Telescope (MAST) Narrow-Band Imager

The Multi-Application Solar Telescope is a 50 cm off-axis Gregorian telescope recently installed at the Udaipur Solar Observatory, India. In order to obtain near-simultaneous observations at photospheric and chromospheric heights, an imager optimized for two or more wavelengths is being integrated with the telescope. Two voltage-tuneable lithium-niobate Fabry–Perot etalons along with a set of interference blocking filters have been used for developing the imager. Both of the etalons are used in tandem for photospheric observations in Fe i 6173 Å and chromospheric observation in H\(\alpha\) 6563 Å spectral lines, whereas only one of the etalons is used for the chromospheric Ca II line at 8542 Å. The imager is also being used for spectropolarimetric observations. We discuss the characterization of the etalons at the above wavelengths, detail the integration of the imager with the telescope, and present a few sets of observations taken with the imager set-up.