Regional diversity in some aspects of Indian demography

Regional variations in the demography of India are explored using official data for the period 1971-1981. Socioeconomic and cultural determinants of such differentials, particularly female education and economic diversification, are addressed.     

Radioactivity monitoring in the vicinity of Jawalamukhi thrust NW Himalaya,India for tectonic study

Natural Hazards - This work reports radon-thoron monitoring at two depths (60 and 90 cm) and at 82 sites around Jawalamukhi thrust of NW Himalaya, India using Solid State Nuclear Track...     

Mass Discharge in a Tracer Plume: Evaluation of the Theissen Polygon Method

A tracer plume was created within a thin aquifer by injection for 299 d of two adjacent “sub‐plumes” to represent one type of plume heterogeneity encountered in practice. The plume was monitored by snapshot sampling of transects of fully screened wells. The mass injection rate and total mass injected were known. Using all wells in each transect (0.77 m well spacing, 1.4 points/m² sampling density), the Theissen Polygon Method (TPM) yielded apparently accurate mass discharge (M_d) estimates at three transects for 12 snapshots. When applied to hypothetical sparser transects using subsets of the wells with average spacing and sampling density from 1.55 to 5.39 m and 0.70 to 0.20 points/m², respectively, the TPM accuracy depended on well spacing and location of the wells in the hypothesized transect with respect to the sub‐plumes. Potential error was relatively low when the well spacing was less than the widths of the sub‐plumes (>0.35 points/m²). Potential error increased for well spacing similar to or greater than the sub‐plume widths, or when less than 1% of the plume area was sampled. For low density sampling of laterally heterogeneous plumes, small changes in groundwater flow direction can lead to wide fluctuations in M_d estimates by the TPM. However, sampling conducted when flow is known or likely to be in a preferred direction can potentially allow more useful comparisons of M_d over multiyear time frames, such as required for performance evaluation of natural attenuation or engineered remediation systems.     

Dynamic response analysis of tube array in partially filled calandria

An efficient procedure is presented for dynamic response analysis of horizontal tube array in partially filled calandria including hydrodynamic interaction effects. The procedure is general enough to consider the transfer of energy between the fluid-coupled tubes, and effects of moderator sloshing on the magnitude and the distribution of hydrodynamic forces. It has been demonstrated that the conventional added mass approach fails to represent behaviour of the tube array correctly, and it is therefore necessary to consider the flexibility of all the tubes along two directions simultaneously. The procedure presented can simulate the added damping effects due to hydrodynamic interaction. The possible use of a tuned damper tube is suggested for controlling sloshing effects for tube array in a calandria where tube frequencies and sloshing frequencies are closely spaced. The presence of surface damping in the tuned tube further brings down the response, and the suggested procedure can be effectively used to control unwarranted sloshing effects. Copyright © 1999 John Wiley & Sons, Ltd.     

Evaluation of linear regression methods as downscaling tools in temperature projections over the Pichola Lake Basin in India

In this paper, downscaling models are developed using various linear regression approaches, namely direct, forward, backward and stepwise regression, for obtaining projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) to lake‐basin scale in an arid region in India. The effectiveness of these regression approaches is evaluated through application to downscale the predictands for the Pichola lake region in the state of Rajasthan in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (i) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (ii) the simulations from the third‐generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. The selection of important predictor variables becomes a crucial issue for developing downscaling models as reanalysis data are based on a wide range of meteorological measurements and observations. A simple multiplicative shift was used for correcting predictand values. Direct regression was found to yield better performance among all other regression techniques for the training data set, while the forward regression technique performed better in the validation data set, explored in the present study. For trend analysis, the Mann–Kendall non‐parametric test was performed. The results of downscaling models show that an increasing trend is observed for Tmax and Tmin for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT scenario by using predictors. Copyright © 2010 John Wiley & Sons, Ltd.     

Cyclonic microzonation

A procedure for cyclonic microzonation of coastal regions with the help of the cyclone track records is outlined using a sound method of statistical forecast and finding wind speed at a site with the help of standard wind field model. The procedure can be adopted for regions where directly measured wind speeds are scarce like, coastal regions of the developing and under developed countries. For the purpose of microzonation, the regions along with the available cyclone tracks are mapped using GIS. The area is then divided into a number of grids. The centre of the grid (site) is taken as the centre of the circle of influence. The cyclonic wind speeds at the site are estimated from the tracks falling within the influence circle. Distribution of the cyclonic wind speed at the site is then obtained from the estimated cyclonic wind speeds. Assuming the arrival of cyclone to be a Poisson process, a cyclone hazard curve, denoting the annual probability of exceedance versus cyclonic wind speed is determined. From the hazard curves drawn for different sites of the region, cyclonic microzonation map is prepared for different return periods of the cyclonic wind speed. The procedure is illustrated by applying it to microzone a very crucial coastal region of Andhra Pradesh in India, for which cyclone track records are available.     

Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.