Proteins in Wastewater and Wastewater Sludges

Although there is some information on the total amounts of proteins in wastewater and sludges1"3 and on the amino acids in them,4-11 especially in activated sludge,12 there is almost no evidence on the nature of the proteins in these materials. A knowledge of the nature of proteins in wastewater, sludges, and similar substances would be useful not only for determining the pollutional effects on the environment and the changes in the protein structures during decomposition or treatment, but also for determining the possible usage of the resulting materials in agriculture,13 includ ing animal nutrition.

Soft classification based Sub-pixel allocation model

Land cover (LC) refers to what is actually present on the ground and provide insights into the underlying solution for improving the conditions of many issues, from water pollution to sustainable economic development. One of the greatest challenges of modeling LC changes using remotely sensed (RS) data is of scale-resolution mismatch: that the spatial resolution of detail is less than what is required, and that this sub-pixel level heterogeneity is important but not readily knowable. However, many pixels consist of a mixture of multiple classes. The solution to mixed pixel problem typically centers on soft classification techniques that are used to estimate the proportion of a certain class within each pixel. However, the spatial distribution of these class components within the pixel remains unknown. This study investigates Orthogonal Subspace Projection - an unmixing technique and uses pixel-swapping algorithm for predicting the spatial distribution of LC at sub-pixel resolution. Both the algorithms are applied on many simulated and actual satellite images for validation. The accuracy on the simulated images is ~100%, while IRS LISS-III and MODIS data show accuracy of 76.6% and 73.02% respectively. This demonstrates the relevance of these techniques for applications such as urban-nonurban, forest-nonforest classification studies etc.

Fuzzy optimization model for water quality management of a river system - Closure

A fuzzy waste-load allocation model, FWLAM, is developed for water quality management of a river system using fuzzy multiple-objective optimization. An important feature of this model is its capability to incorporate the aspirations and conflicting objectives of the pollution control agency and dischargers. The vagueness associated with specifying the water quality criteria and fraction removal levels is modeled in a fuzzy framework. The goals related to the pollution control agency and dischargers are expressed as fuzzy sets. The membership functions of these fuzzy sets are considered to represent the variation of satisfaction levels of the pollution control agency and dischargers in attaining their respective goals. Two formulations—namely, the MAX-MIN and MAX-BIAS formulations—are proposed for FWLAM. The MAX-MIN formulation maximizes the minimum satisfaction level in the system. The MAX-BIAS formulation maximizes a bias measure, giving a solution that favors the dischargers. Maximization of the bias measure attempts to keep the satisfaction levels of the dischargers away from the minimum satisfaction level and that of the pollution control agency close to the minimum satisfaction level. Most of the conventional water quality management models use waste treatment cost curves that are uncertain and nonlinear. Unlike such models, FWLAM avoids the use of cost curves. Further, the model provides the flexibility for the pollution control agency and dischargers to specify their aspirations independently.

Intelligent thermal energy meter cum controller for solar water heating systems

A microcontroller based, thermal energy meter cum controller (TEMC) suitable for solar thermal systems has been developed. It monitors solar radiation, ambient temperature, fluid flow rate, and temperature of fluid at various locations of the system and computes the energy transfer rate. It also controls the operation of the fluid-circulating pump depending on the temperature difference across the solar collector field. The accuracy of energy measurement is +/-1.5%. The instrument has been tested in a solar water heating system. Its operation became automatic with savings in electrical energy consumption of pump by 30% on cloudy days.

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA)problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max-min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.

Ecological Assessment of Lentic Water Bodies of Bangalore

The restoration, conservation and management of water resources require a thorough understanding of what constitutes a healthy ecosystem. Monitoring and assessment provides the basic information on the condition of our waterbodies. The present work details the study carried out at two waterbodies, namely, the Chamarajasagar reservoir and the Madiwala Lake. The waterbodies were selected on the basis of their current use and locations. Chamarajasagar reservoir serves the purpose of supplying drinking water to Bangalore city and is located on the outskirts of the city surrounded by agricultural and forest land. On the other hand, Madiwala lake is situated in the heart of Bangalore city receiving an influx of pollutants from domestic and industrial sewage. Comparative assessment of the surface water quality of both were carried out by instituting the various physico–chemical and biological parameters. The physico-chemical analyses included temperature, transparency, pH, electrical conductivity, dissolved oxygen, alkalinity, total hardness, calcium hardness, magnesium hardness, nitrates, phosphates, sodium, potassium and COD measurements of the given waterbody. The analysis was done based on the standard methods prescribed (or recommended) by (APHA) and NEERI. The biological parameter included phytoplankton analysis. The detailed investigations of the parameters, which are well within the tolerance limits in Chamarajasagar reservoir, indicate that it is fairly unpolluted, except for the pH values, which indicate greater alkalinity. This may be attributed to the natural causes and the agricultural runoff from the catchment. On the contrary, the limnology of Madiwala lake is greatly influenced by the inflow of sewage that contributes significantly to the dissolved solids of the lake water, total hardness, alkalinity and a low DO level. Although, the two study areas differ in age, physiography, chemistry and type of inflows, they still maintain a phytoplankton distribution overwhelmingly dominated by Cyanophyceae members,specifically Microcystis aeruginosa. These blue green algae apparently enter the waterbodies from soil, which are known to harbour a rich diversity of blue green flora with several species common to limnoplankton, a feature reported to be unique to the south Indian lakes.Chamarajasagar water samples revealed five classes of phytoplankton, of which Cyanophyceae (92.15 percent) that dominated other algal forms comprised of one single species of Microcystis aeruginosa. The next major class of algae was Chlorophyceae (3.752 percent) followed by Dinophyceae (3.51 percent), Bacillariophyceae (0.47 percent) and a sparsely available and unidentified class (0.12 percent).Madiwala Lake phytoplankton, in addition to Cyanophyceae (26.20 percent), revealed a high density of Chlorophyceae members (73.44 percent) dominated by Scenedesmus sp.,Pediastrum sp., and Euglena sp.,which are considered to be indicators of organic pollution. The domestic and industrial sewage, which finds its way into the lake, is a factor causing organic pollution. As compared to the other classes, Euglenophyceae and Bacillariophyceae members were the lowest in number. Thus, the analysis of various parameters indicates that Chamarajasagar reservoir is relatively unpolluted except for the high percentage of Microcystis aeruginosa, and a slightly alkaline nature of water. Madiwala lake samples revealed eutrophication and high levels of pollution, which is clarified by the physico–chemical analysis, whose values are way above the tolerance limits. Also, the phytoplankton analysis in Madiwala lake reveals the dominance of Chlorophyceae members, which indicate organic pollution (sewage being the causative factor).

Evaluation of the Quality of Drinking Water in Kerala State, India

The quality of tap water from water supplies from 14 districts of Kerala state, India was studied. Parameters like pH, water temperature, total dissolved solids, salinity, nitrates, chloride, hardness, magnesium, calcium, sodium, potassium, fluoride, sulphate, phosphates, and coliform bacteria were enumerated. The results showed that all water samples were contaminated by coliform bacteria. About 20% of the tap water samples from Alappuzha and 15% samples from Palakkad district are above desirable limits prescribed by Bureau of Indian Standards. The contamination of the source water (due to lack of community hygiene) and insufficient treatment are the major cause for the coliform contamination in the state. Water samples from Alappuzha and Palakkad have high ionic and fluoride content which could be attributed to the geology of the region. Water supplied for drinking in rural areas are relatively free of any contamination than the water supplied in urban area by municipalities, which may be attributed higher chances of contamination in urban area due to mismanagement of solid and liquid wastes. The study highlights the need for regular bacteriological enumeration along with water quality in addition to setting up decentralised region specific improved treatment system.

River water quality response under hypothetical climate change scenarios in Tunga-Bhadra river, India

Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low- flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga-Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright (C) 2011 John Wiley & Sons, Ltd.

Environmental monitoring of bacterial contamination and antibiotic resistance patterns of the fecal coliforms isolated from Cauvery River, a major drinking water source in Karnataka, India

The present study focuses prudent elucidation of microbial pollution and antibiotic sensitivity profiling of the fecal coliforms isolated from River Cauvery, a major drinking water source in Karnataka, India. Water samples were collected from ten hotspots during the year 2011-2012. The physiochemical characteristics and microbial count of water samples collected from most of the hotspots exhibited greater biological oxygen demand and bacterial count especially coliforms in comparison with control samples (p <= 0.01). The antibiotic sensitivity testing was performed using 48 antibiotics against the bacterial isolates by disk-diffusion assay. The current study showed that out of 848 bacterial isolates, 93.51 % (n=793) of the isolates were found to be multidrug-resistant to most of the current generation antibiotics. Among the major isolates, 96.46 % (n=273) of the isolates were found to be multidrug-resistant to 30 antibiotics and they were identified to be Escherichia coli by 16S rDNA gene sequencing. Similarly, 93.85 % (n=107), 94.49 % (n=103), and 90.22 % (n=157) of the isolates exhibited multiple drug resistance to 32, 40, and 37 antibiotics, and they were identified to be Enterobacter cloacae, Pseudomonas trivialis, and Shigella sonnei, respectively. The molecular studies suggested the prevalence of blaTEM genes in all the four isolates and dhfr gene in Escherichia coli and Sh. sonnei. Analogously, most of the other Gram-negative bacteria were found to be multidrug-resistant and the Gram-positive bacteria, Staphylococcus spp. isolated from the water samples were found to be methicillin and vancomycin-resistant Staphylococcus aureus. This is probably the first study elucidating the bacterial pollution and antibiotic sensitivity profiling of fecal coliforms isolated from River Cauvery, Karnataka, India.