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.

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.

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte-Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga-Bhadra river system in southern India, with a steady state BOD-DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality. (c) 2005 Elsevier Ltd. All rights reserved.

Water quality parameter estimation in steady-state distribution system

The maintenance of chlorine residual is needed at all the points in the distribution system supplied with chlorine as a disinfectant. The propagation and level of chlorine in a distribution system is affected by both bulk and pipe wall reactions. It is well known that the field determination of wall reaction parameter is difficult. The source strength of chlorine to maintain a specified chlorine residual at a target node is also an important parameter. The inverse model presented in the paper determines these water quality parameters, which are associated with different reaction kinetics, either in single or in groups of pipes. The weighted-least-squares method based on the Gauss-Newton minimization technique is used for the estimation of these parameters. The validation and application of the inverse model is illustrated with an example pipe distribution system under steady state. A generalized procedure to handle noisy and bad (abnormal) data is suggested, which can be used to estimate these parameters more accurately. The developed inverse model is useful for water supply agencies to calibrate their water distribution system and to improve their operational strategies to maintain water quality.

Protocols for Collection, Preservation and Enumeration of Diatoms from Aquatic Habitats fro Water Quality Monitoring in India

Diatoms have become important organisms for monitoring freshwaters and their value has been recognised in Europe, American and African continents. If India is to include diatoms in the current suite of bioindicators, then thorough testing of diatom-based techniques is required. This paper provides guidance on methods through all stages of diatom collection from different habitats from streams and lakes, preparation and examination for the purposes of water quality assessment that can be adapted to most aquatic ecosystems in India.

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.

Climate change induced risk in water quality control problems

A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.

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.

Optimal scheduling of multiple chlorine sources in water distribution systems

The specified range of free chlorine residual (between minimum and maximum) in water distribution systems needs to be maintained to avoid deterioration of the microbial quality of water, control taste and/or odor problems, and hinder formation of carcino-genic disinfection by-products. Multiple water quality sources for providing chlorine input are needed to maintain the chlorine residuals within a specified range throughout the distribution system. The determination of source dosage (i.e., chlorine concentrations/chlorine mass rates) at water quality sources to satisfy the above objective under dynamic conditions is a complex process. A nonlinear optimization problem is formulated to determine the chlorine dosage at the water quality sources subjected to minimum and maximum constraints on chlorine concentrations at all monitoring nodes. A genetic algorithm (GA) approach in which decision variables (chlorine dosage) are coded as binary strings is used to solve this highly nonlinear optimization problem, with nonlinearities arising due to set-point sources and non-first-order reactions. Application of the model is illustrated using three sample water distribution systems, and it indicates that the GA,is a useful tool for evaluating optimal water quality source chlorine schedules.

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).

Water treatment potential in relation to phytoplankton dynamics in tropical sewage-fed urban water bodies

Urban water bodies frequently receive untreated sewage and water levels in such water bodies are maintained by daily inputs of sewage. They function as “variable-zone” anaerobic-aerobic lagoons suffering several macrophyte, biotic and abiotic stresses. We have studied two such lakes in Bangalore (Bellandur-360 ha and Varthur-220 ha) to understand whether such an occurrence could be made beneficial (maintaining water levels as well as treatment). Such hypertrophic water body receives sewage at 180-250mg/L and is discharged at 25-80mg/L COD/BOD in different seasons. In an earlier study we reported macrophyte altering the purification function of the water body. In this paper we studied the impact of phytoplankton dynamics and macrophyte cover on the functions such as organic load removal. Algal community analysis, algal biomass, macrophyte cover, water quality, nutrient status was studied seasonally during 2009-2010. Oxygen deficiency and sometimes anoxia, recorded from surface samples resulted in high quantities of NH4+-N (30-40mg/L) and phosphate (0.5-4mg/L)-characteristics of anoxic hypertrophic urban lakes. The productiveness favoured high phytoplanktonic community characterized by small cells (<10μm; Chlorella sp. - highest numbers). The lake could be clearly demarcated into an initial anaerobic zone (40% area), a facultative zone (20%) and an aerobic zone (40%) based on redox values and GIS/bathymetry. During summer the lake is covered by floating macrophytes converting the lake into an anoxic/anaerobic water pool subduing the water purification function as well as aesthetics. When macrophytes are controlled such sewage fed water bodies can be used for treating urban wastewater while also maintaining water sustainability in these semi-arid ecosystems. This paper reports the community dynamics of phytoplankton, their function and competition with macrophytes.

Impacts of climate change on regional water resources

Water is the most important medium through which climate change influences human life. Rising temperatures together with regional changes in precipitation patterns are some of the impacts of climate change that have implications on water availability, frequency and intensity of floods and droughts, soil moisture, water quality, water supply and water demands for irrigation and hydropower generation. In this article we provide an introduction to the emerging field of hydrologic impacts of climate change with a focus on water availability, water quality and irrigation demands. Climate change estimates on regional or local spatial scales are burdened with a considerable amount of uncertainty, stemming from various sources such as climate models, downscaling and hydrological models used in the impact assessments and uncertainty in the downscaling relationships. The present article summarizes the recent advances on uncertainty modeling and regional impacts of climate change for the Mahanadi and Tunga-Bhadra Rivers in India.

Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.