Co-composting of cow dung,municipal solid waste,roadside pond sediment and tannery sludge: role of human hair

In general, farmers from developing countries often use several feeding mixtures based on materials available in their vicinity resulting in a final poor-quality compost product. Human hair as a composting feed could impact on the nutrient status in prepared compost. In this study, the effect of different amount of human hair to tannery sludge, roadside pond sediment, municipal solid waste and cow dung was investigated during 70-day composting cycle. Human hair addition increased N, P and K from 1.36 to 22.85, 53.06 to 189.80 and 4.13 to 39.26%, respectively, over control. Total metal and arsenic contaminations were significantly higher than in control but lower than the Indian permissible limit. Highest amount of human hair in composting feed indicated that the germination index for tea seed (Camellia sinensis L.) was less than 80% reflecting the possible remaining phytotoxic substances. Redundancy analysis revealed that there was a significance influence of the physico-chemical variables on bacterial community.     

Assessment of vulnerability zones for ground water pollution using GIS-DRASTIC-EC model: A field-based approach

Assessment of groundwater vulnerability to pollution is an essential pre-requisite for better planning of an area. We present the groundwater vulnerability assessment in parts of the Yamuna Nagar District, Haryana State, India in an area of about \(800 \hbox { km}^{2}\), considered to be a freshwater zone in the foothills of the Siwalik Hill Ranges. Such areas in the Lower Himalayas form good groundwater recharge zones, and should always be free from contamination. But, the administration has been trying to promote industrialization along these foothill zones without actually assessing the environmental consequences such activities may invite in the future. GIS-DRASTIC model has been used with field based data inputs for studying the vulnerability assessment. But, we find that inclusion electrical conductivity (EC) as a model parameter makes it more robust. Therefore, we rename it as GIS-DRASTIC-EC model. The model identifies three vulnerability zones such as low, moderate and high with an areal extent of 5%, 80% and 15%, respectively. On the basis of major chemical parameters alone, the groundwater in the foothill zones apparently looks safe, but analysis with the help of GIS-DRASTIC-EC model gives a better perspective of the groundwater quality in terms of identifying the vulnerable areas.     

Artificial Neural Network Prediction Models for Soil Compaction and Permeability

This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD) and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions for using these models are discussed in detail in this paper.     

Assessing the resilience of Delhi to climate-related disasters: a comprehensive approach

The study addresses disaster risks in Delhi through a resilience approach. It utilizes the Climate Disaster Resilience Index (CDRI) tool, which assesses disaster resilience from five dimensions: physical, social, economic, institutional, and natural. Each dimension comprises 5 parameters, and each parameter consists of 5 variables. The study is carried out in the nine revenue districts of Delhi and reveals that East Delhi is least resilient and New Delhi is most resilient. The CDRI analysis in East Delhi points out the urgent need to focus on key parameters such as housing and land use, population, intensity and frequency of natural hazards, ecosystem services, and land use in natural terms. On the other hand, New Delhi is the most resilient due to all five dimensions, where most significant parameters responsible for its high resilience are housing and land use, population, income, employment, intensity and frequency of natural hazards, ecosystem services, and land use in natural terms. In addition, the overall results of all nine districts show an inverse relationship between resilience score and population density. For example, districts with higher population density show low resilience and vice versa. Moreover, districts located on hazard-prone areas show low resilience. For example, East Delhi and North East Delhi scored low resilience because they both are situated on the Yamuna flood catchment areas. The study further develops key suggestions that are required to address disaster risk in all nine districts of Delhi and discusses future implications of CDRI to address city??s vulnerability. The approach??s distinctness is reflected through its consideration of micro-level diversities and presents some implications to resilience.     

Genesis of some tertiary Indian coals from the chemical composition of ash – a statistical approach: Part 1

In the present investigation, 37 numbers of high sulphur tertiary coal samples from Meghalaya, India have been studied on the basis of proximate and ash analysis. Various statistical tools like Bivariant Analysis, Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and also the geochemical indicators were applied to determine the dominant detrital or authigenic affinity of the ash forming elements in these coals. The genetic interpretation of coal as well as the coal ash has been carried out based on chemical compositions of high temperature ash (HTA) by using Detrital/Authigenic Index. X-Ray Diffraction (XRD) analysis was also carried out to study the mineralogy of the studied coal ashes. Both statistical tools and geochemical indicators have confirmed the detrital nature of these coals as well as the ash forming elements.     

Radioelement-bearing secondary zoned apatite around Govardhanagiri-Chinnakolumulapalli-L. Banda areas of Kurnool District,Andhra Pradesh

Zoned Apatite crystals with intrinsic radioactivity are widespread in the S-type basement granite around Govardhanagiri-Chinnakolumulapalli-L.Banda areas of Kurnool District, A.P. The granite is overlain by Gulcheru quartzite and in turn by Vempalle dolostone of Papaghni Group belonging to the Cuddapah Supergroup. The area (4 km x 6 km) is characterized by conjugate fracture system comprising ENE-WSW and WNW-ESE of varying extents, of which, ENE-WSW is sympathetic to the major deep seated Gani Kalva fault. The apatite crystals are frequently zoned and vary in size from few microns to 1000 microns, occur mostly as veins or associated with pulverized matrix of granite cataclasite. Veins (upto 2 cm. thick) generally follow the major fracture-trend (ENE-WSW) in the area. The apatites are also found intimately associated with fluorite, tourmaline and sulphides, which suggest their derivation from pneumatolytic fluids. The intrinsic accessory uranium as scarce grains of uraninite and other labile phases of granite were remobilized consequent upon the deformation and locked up as ferroan-oxy/hydroxyl inclusions in slowly growing apatite crystals under very low temperature conditions.     

Diatoms from surface sediments of Enderby Basin of Indian Sector of Southern Ocean

Diatoms from surface sediment samples collected from Enderby Basin of Indian Sector of Southern Ocean were analyzed to determine the relative abundance and distribution of seven key indicator diatom species viz. Sea ice related species Fragilariopsis rhombica, F. separanda, F. curta, F. ritscheri, Thalassiosira tumida and Actinocyclus actinochilus and Open Ocean species F. kerguelensis on the basis of modern physico-chemical parameters. The relative abundances of different species observed viz. F. rhombica — 6.25%; F. separanda — 12.5%; F. curta — 10.53–13.33%; F. ritscheri — 4.55–12.5%; F. kerguelensis — 6.25–63.64%; T. tumida — 3.13% and A. actinochilus — 9.38–13.33%. The increasing abundance of F. kerguelensis consecutively suggests the effect of Antarctic bottom water in the study area which is further substantiated by the presence and increasing abundance of F. ritscheri. The gradual decrease in abundance or absence of sea ice related species from the sampled stations indicates the summer and winter sea ice extent concentration in the study area. The nutrient concentration correlates with the distribution and abundances of diatom species.     

Estimation of mass and energy balance of glaciers using a distributed energy balance model over the Chandra river basin (Western Himalaya)

The ongoing glacier shrinking in the Himalayan region causes a significant threat to freshwater sustainability and associated future runoff. However, data on the spatial climatic contribution of glacier retreat is scanty in this region. To investigate the spatially distributed glacier surface energy and mass fluxes, a two-dimensional mass balance model was developed and applied to the selected glaciers of the Chandra basin, in the Upper Indus Basin, Western Himalaya. This model is driven by the remote sensing data and meteorological variables measured in the vicinity of the Chandra basin for six hydrological years (October 2013 to September 2019). The modelled variables were calibrated/validated with the in-situ observation from the Himansh station in the Chandra basin. We have derived air temperature (T_a ) spatially using the multivariate statistical approach, which indicates a relative error of 0.02–0.05°C with the observed data. Additionally, the relative error between the modelled and observed radiation fluxes was <10.0 W m⁻². Our study revealed that the Chandra basin glaciers have been losing its mass with a mean annual mass balance of −0.59 ± 0.12 m w.e. a⁻¹ for the six hydrological years. Results illustrated that the mean surface melt rate of the selected glaciers ranged from −5.1 to −2.5 m w.e. a⁻¹ that lies between 4500 and 5000 m a.s.l. The study revealed that the net radiation (R_N) contributes ~75% in total energy (F_M ) during the melt season while sensible heat (H_S) , latent heat (H_l) , and ground heat (H_G) fluxes shared 15%, 8%, and 2%, respectively. Sensitivity analysis of the energy balance components suggested that the mass balance is highly sensitive to albedo and surface radiations in the study area. Overall, the proposed model performed well for glacier-wide energy and mass balance estimation and confirms the utility of remote sensing data, which may help in reducing data scarcity in the upper reaches of the Himalayan region.     

Structural information derived from ambient noise tomography over a hydrocarbon-producing region in the Cachar fold belt,lower Assam,northeast India

Ambient noise tomography is a powerful tool that has found increasing application in reservoir analysis and imaging. The Cachar fold belt in lower Assam, northeast India encompasses several wells under active hydrocarbon production, along with several dry wells. To overcome the lack of active seismic data over the entire fold belt, a passive seismic study was carried out to image the concealed three-dimensional sub-surface structures. The data were recorded from February to November 2011 by a network of 65 wideband seismometers spanning an area of about 40 × 60 km². The data are crosscorrelated in the 2–5 s band, followed by phase-weighted stacking to estimate noise correlation functions with surface wave signatures. The traveltimes picked from the frequency-time analysis are utilized in a tomographic inversion for Rayleigh wave group velocities. The group velocity anomalies have a lateral resolution of ~ 3.5 × 5.5 km² and variations of up to $\pm 20\%$ for each period. The group velocities are in turn inverted for S-wave velocity distribution as a function of depth. The three-dimensional S-wave velocity tomograms reveal the tight anticlines and broad synclines, with high- and low-velocity zones corresponding to structural highs and lows, respectively. The structural interpretation is supported for the part of the region with producing wells and covered by active seismic data, wherein the post-stack time migrated seismic section shows anticlinal and synclinal features similar to those obtained from ambient noise tomography. The structures revealed by ambient noise tomography can help identify zones of interest to be targeted by active seismic surveys in the Cachar fold belt.