DERIVATION OF THE GAMMA DISTRIBUTION BY USING THE PRINCIPLE OF MAXIMUM ENTROPY (POME)1

The principle of maximum entropy (POME) was used to derive the two-parameter gamma distribution used frequently in synthesis of instantaneous or finite-period unit hydrographs. The POME yielded the minimally prejudiced gamma distribution by maximizing the entropy subject to two appropriate constraints which were the mean of real values and the mean of the logarithms of real values of the variable. It provided a unique method for parameter estimation. Experimental data were used to compare this method with the methods of moments, cumulants, maximum likelihood estimation, and least squares.     

Disaster Management for Nandira Watershed District Angul (Orissa) India, Using Temporal Remote Sensing Data and Gis

NALCO – the largest exporter of aluminium in India has a power plant of 720 MW capacity in Nandira watershed in Angul district of Orissa. The power plant utilises local coal to generate thermal power and disposes of large amount of ash which accumulates in slurry form at nearby two ash ponds. These ash ponds were breached on 31 December 2000, causing ash accumulation for entire regime of the Nandira river. An attempt has been made towards preparation of recovery and rehabilitation plan for NALCO using temporal Remote Sensing data and GIS. Indian remote sensing satellite data for pre-breach condition 12 December 2000, during breach event 31 December 2000 and post-breach condition 4 and 6 January 2001 has been digitally analysed for Nandira watershed. The satellite data of coarse spatial resolution provides the absence and presence of fresh sediment deposition along Nandira watershed and Brahmani river pertaining to pre-breach and post-breach conditions respectively on regional scales. The temporal comparison of fine resolution has clearly highlighted the aerial extent of damage caused by the disaster for entire watershed on local scales. The GIS has helped in demarcation of freshly accumulated ash at interval of 500 m along the river length as well as in delineation of maximum ash accumulation across the river width. The study has clearly demonstrated the use of temporal Remote Sensing data in conjunction with GIS for disaster management in terms of recovery and rehabilitation plan preparation of the Nandira watershed.     

Biomonitoring of Lead in Atmospheric Environment of An Urban Center of the Ganga Plain, India

Monitoring of atmospheric lead from the Dalbergia sissoo treewas undertaken at Lucknow urban centre of the Ganga Plain, India.Atotal of 26 leaf samples were collected in spring, monsoon and winter seasons from 16 sampling sites and was analysed by Atomic Absorption Spectrophotometry method. Lead concentrations were low in spring season, increased in monsoon to winter seasons and range from 2.1 to 28.2 μg/g (dry wt.). This accumulative response of lead in the tree leaves is directly linked with the exposure time of automobile emission that is considered to be the predominant source for it. Highway localities show higher lead concentrations by a factor of 2 as compared to urban localities. Highest concentration was recorded at Sitapur Road (National Highway No. 24) in winter season. A linear quantitative relationship between urban air-lead levels and lead-in-the Dalbergia sissoo leaves is used to infer the qualitative assessment of present day atmospheric lead pollution. Reported results suggest a drastic reduction in mean lead concentration in Lucknow urban air from 1.32μg/m3 in 1994 to 0.19μg/m3 in 2002. Similarly, mean lead concentration in the tree leaves during winter season also dropped from 17.9 μg/g in 1994 to 8.1 μg/g in 2004. Despite of increasing urban population, urban area, vehicle population and traffic density, the introduction of unleaded-petrol (vehicular fuel) keeps lead level in the urban environment of Lucknow much lower than the past. Like Lucknow, other urban centres of the Ganga Plain are also on way to the exponential increase in pressure of urbanization. An appropriate urban public transport planning is required to provide healthy atmospheric environment for millions of people especially future young generation.     

Validation of two air quality models for Indian mining conditions

All major mining activity particularly opencast mining contributes to the problem of suspended particulate matter (SPM)directly or indirectly. Therefore, assessment and prediction are required to prevent and minimize the deterioration of SPM due tovarious opencast mining operations. Determination of emission rate of SPM for these activities and validation of air quality models are the first and foremost concern. In view of the above, the study was taken up for determination of emission rate for SPMto calculate emission rate of various opencast mining activitiesand validation of commonly used two air quality models for Indianmining conditions. To achieve the objectives, eight coal and three iron ore mining sites were selected to generate site specific emission data by considering type of mining, method of working, geographical location, accessibility and above all resource availability. The study covers various mining activitiesand locations including drilling, overburden loading and unloading, coal/mineral loading and unloading, coal handling orscreening plant, exposed overburden dump, stock yard, workshop, exposed pit surface, transport road and haul road. Validation of the study was carried out through Fugitive Dust Model (FDM) and Point, Area and Line sources model (PAL2) by assigning the measured emission rate for each mining activity, meteorologicaldata and other details of the respective mine as an input to the models. Both the models were run separately for the same set ofinput data for each mine to get the predicted SPM concentrationat three receptor locations for each mine. The receptor locationswere selected such a way that at the same places the actual filedmeasurement were carried out for SPM concentration. Statisticalanalysis was carried out to assess the performance of the modelsbased on a set measured and predicted SPM concentration data. The value of coefficient of correlation for PAL2 and FDM was calculated to be 0.990-0.994 and 0.966-0.997, respectively, which shows a fairly good agreement between measured and predicted values of SPM concentration. The average index of agreement values for PAL2 and FDM was found to be 0.665 and0.752, respectively, which represents that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively. These indicate that FDM model is more suited for Indian mining conditions.     

Selenium status in soils of northern districts of India

The HG-AAS technique was used to estimate the soil selenium status of the agricultural lands of northern parts of India. The drier lands where lesser rains were received or where less irrigation water was available in Rajasthan and southern parts of the Haryana states had above normal soil selenium levels. These soils were also found to be alkaline. Punjab, Himachal Pradesh and northern parts of the Haryana states had normal levels of selenium in their soils, except with slightly lower selenium levels in a few areas that were affected by floods along the river Yamuna. The results were also confirmed using the ICP-OES technique.     

Non-edible vegetable oil–based feedstocks capable of bio-lubricant production for automotive sector applications—a review

Environmental Science and Pollution Research - Fossil fuel resource is on the draining stage which leads to an increment in the cost of the petroleum products. Nowadays, research is focused on the...     

Virulence-associated traits and in vitro biofilm-forming ability of Escherichia coli isolated from a major river traversing Northern India

Environmental Science and Pollution Research - Several strains of Escherichia coli harbor virulence traits, resulting in E. coli–related intestinal and extra-intestinal infections. Various...     

Potential Impact of Climate Change on Residential Energy Consumption in Dhaka City

This study aimed to assess the impacts of climate change on residential energy consumption in Dhaka city of Bangladesh. The monthly electricity consumption data for the period 2011–2014 and long-term climate variables namely monthly rainfall and temperature records (1961–2010) were used in the study. An ensemble of six global circulation models (GCMs) of coupled model intercomparison project phase 5 (CMIP5) namely, BCCCSM1-1, CanESM2, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, and NorESM1-M under four representative concentration pathway (RCP) scenarios were used to project future changes in rainfall and temperature. The regression models describing the relationship between historical energy consumption and climate variables were developed to project future changes in energy consumptions. The results revealed that daily energy consumption in Dhaka city increases in the range of 6.46–11.97 and 2.37–6.25 MkWh at 95% level of confidence for every increase of temperature by 1 °C and daily average rainfall by 1 mm, respectively. This study concluded that daily total residential energy demand and peak demand in Dhaka city can increase up to 5.9–15.6 and 5.1–16.7%, respectively, by the end of this century under different climate change scenarios.     

Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM

The crop pattern has a significant impact on the feasibility of sustainable agricultural practices. Selected crop pattern influences environmental and economic condition and affects sustainability profoundly in agricultural practices. Hence, a careful intervention is required in the selection of an optimal crop pattern for sustainable agricultural practices. Selection of a particular set of crop pattern depends on many criteria that may vary from place to place thus pose challenges in deciding an optimum crop pattern. The present research focuses on the crop selection pattern in Indian environment that considers comprehensive criteria related to sustainable farming practices. Based on the in-depth review of the literature and experts opinion, comprehensive criteria related to sustainable farming practices for Ravi season crop are identified. Total twelve criteria covering socioeconomic conditions, soil and water conditions, environmental and climatic conditions are earmarked and taken into account for eight most commonly grown crops in Ravi season and later on modeled to determine the crop pattern for most needed sustainability. A fuzzy-based multi-criteria decision-making model has been developed considering the Indian farming system. The scarce resources availability to Indian farmers poses many challenges to practice farming with most needed sustainability. The present research will be useful in the area of Indian farming practices in particular and global farming practices in general. It will also help stakeholders in their cost effective decision making for better crop productivity leading to sustainable farming practices. Additionally, the state policy makers will be able to formulate effective state driven sustainable farming policy to enhance its stake in gross domestic product to become self-reliance.