Recovering from disasters: a study of livelihoods in post‐quake villages in northern Pakistan

The October 2005 earthquake in northern Pakistan severely affected the livelihoods of 1.5 million people. With the destruction of material assets and communications infrastructure, the quake had a devastating impact on people's way of life in this remote mountainous region. This paper explores livelihood revival interventions undertaken during the earthquake response, and considers how differentiated livelihood outcomes were achieved. In addressing this objective the paper examines livelihood rehabilitation schemes in terms of structural aspects, working strategies, key factors, strengths of interventions and the role of human agency in influencing livelihood trajectories of quake‐affected communities. Primary data for this study was gathered in northern Pakistan between October 2008 and January 2009. The study identifies structural shortcomings and strengths of the programmes attempting to revive the livelihoods of poor and vulnerable households. It identifies how households in two villages made the most of opportunities to improve their lives and move towards favourable outcomes.     

Assessment of four turbulence models in simulation of large-scale pool fires in the presence of wind using computational fluid dynamics (CFD)

Pool fires are the most common of all process industry accidents. Pool fires often trigger explosions which may result in more fires, causing huge losses of life and property. Since both the risk and the frequency of occurrence of pool fires are high, it is necessary to model the risks associated with pool fires so as to correctly predict the behavior of such fires.Among the parameters which determine the overall structure of a pool fire, the most important is turbulence. It determines the extent of interaction of various parameters, including combustion, wind velocity, and entrainment of the ambient air. Of the various approaches capable of modeling the turbulence associated with pool fires, computational fluid dynamics (CFD) has emerged as the most preferred due to its ability to enable closer approximation of the underlying physical phenomena.A review of the state of the art reveals that although various turbulence models exist for the simulation of pool fire no single study has compared the performance of various turbulence models in modeling pool fires. To cover this knowledge-gap an attempt has been made to employ CFD in the assessment of pool fires and find the turbulence model which is able to simulate pool fires most faithfully. The performance of the standard k–? model, renormalization group (RNG) k–? model, realizable k–? model and standard k–ω model were studied for simulating the experiments conducted earlier by Chatris et al. (2001) and Casal (2013). The results reveal that the standard k–? model enabled the closest CFD simulation of the experimental results.     

New kinetic-spectrophotometric method for monitoring the concentration of iodine in river and city water samples

A new kinetic method has been developed for the determination of iodine in water samples. The method is based on the catalytic effect of I^? with the oxidation of Indigo Carmine (IC) by KBrO₃ in the sulfuric acid medium. The optimum conditions obtained are 0.16 M sulfuric acid, 1?×?10^?3 M of IC, 1?×?10^?2 M KBrO₃, reaction temperature of 35°C, and reaction time of 80 s at 612 nm. Under the optimized conditions, the method allowed the quantification of I^? in a range of 12–375 ng/mL with a detection limit of 0.46 ng/mL. The method was applied to the determination of iodine in river and city water samples with the satisfactorily results.     

Attenuation of Gaseous Pollutants by Greenbelts

The article draws attention towards the importance ofgreenbelts in attenuating gaseous pollutants andpresents the meteorological, physico-chemical,biological, and horticultural dimensions associatedwith effective greenbelt design.We have presented the gist of a system ofmethodologies, developed by us, for greenbelt design. A case study demonstrating the applicability of thesystem has been presented.     

Identification and Classification of Key Variables and their Role in Environmental Impact Assessment: Methodology and Software Package Intra

There is, as yet, no proven methodology to enable, objectively,the identification of key parameters out of a large number one normally encounters during any EIA. As EIA is a costly and time-consuming exercise, it is necessary to separate the man from the boys – so to speak – in order to optimize costs andefforts.In this paper a methodology for distinguishing the moreimportant parameters from the less important ones, developed byus, is described. The methodology aims at identifying andshortlisting the key parameters which ought to be studied in agiven EIA situation, thereby helping in reducing time, effort,and cost of EIA. With this methodology a system structure is developed whichgives hierarchical pattern of inter-parameter interaction, andreveals several distinguishing features of each parameter. Asoftware package INTRA (INTer-parameter Relationship Analysis)based on this methodology, has been developed. The paper alsodescribes a case study in which INTRA has been used to study theenvironmental impacts of urbanization of a typical third worldtown (Roorkee).     

Isolation and identification of Aeromonas caviae strain KS-1 as TBTC- and lead-resistant estuarine bacteria

Tributyltin chloride (TBTC)- and lead-resistant estuarine bacterium from Mandovi estuary, Goa, India was isolated and identified as Aeromonas caviae strain KS-1 based on biochemical characteristics and FAME analysis. It tolerates TBTC and lead up to 1.0 and 1.4 mM, respectively, in the minimal salt medium (MSM) supplemented with 0.4 % glucose. Scanning electron microscopy clearly revealed a unique morphological pattern in the form of long inter-connected chains of bacterial cells on exposure to 1 mM TBTC, whereas cells remained unaltered in presence of 1.4 mM Pb(NO₃)₂ but significant biosorption of lead (8 %) on the cell surface of this isolate was clearly revealed by scanning electron microscopy coupled with energy dispersive X-ray spectroscopy. SDS-PAGE analysis of whole-cell proteins of this lead-resistant isolate interestingly demonstrated three lead-induced proteins with molecular mass of 15.7, 16.9 and 32.4 kDa, respectively, when bacterial cells were grown under the stress of 1.4 mM Pb (NO₃)₂. This clearly demonstrated their possible involvement exclusively in lead resistance. A. caviae strain KS-1 also showed tolerance to several other heavy metals, viz. zinc, cadmium, copper and mercury. Therefore, we can employ this TBTC and lead-resistant bacterial isolate for lead bioremediation and also for biomonitoring TBTC from lead and TBTC contaminated environment.     

Ability of Indian pennywort Bacopa monnieri (L.) Pennell in the phytoremediation of sewage (greywater)

Environmental Science and Pollution Research - The freely and abundantly available amphibious plant Indian pennywort Bacopa monnieri (L.) Pennell was able to phytoremediate sewage (greywater)...     

SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1

ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.     

A criterion for developing credible accident scenarios for risk assessment

Maximum credible accident analysis is one of the most widely used concepts in risk assessment of chemical process industries. Central to this concept is the aspect of ‘credibility’ of envisaged accident scenarios. However, thus far the term credibility is mostly treated qualitatively, based on the subjective judgement of the concerned analysts. This causes wide variation in the results of the studies conducted on the same industrial unit by different analysts.

This paper presents an attempt to develop a criterion using which credible accident scenarios may be identified from among a large number of possibilities. The credible scenarios thus identified may then be processed for detailed consequence analysis. This would help in reducing the cost of the analysis and prevent undue emphasis on less credible scenarios at the expense of more credible ones.