The objective of this study was to develop a comprehensive, basin-wide, water-quality-planning model using system dynamics methodology. Later, the model was to be interfaced with a more conventional system dynamics model: one simulating social, technological, economic, and political interactions. By doing so, it is envisioned that such management policies as zoning, abatement facilities, and best management practices may be simulated together. / Master of Science

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.

The spatial and temporal variations in water quality of selected rivers in the Durban Metropolitan Area were investigated using a Geographical Information System. Dissolved oxygen (DO), turbidity, pH, phosphorus and Escherichia coli (E.coli) were selected as water quality parameters for the study. The study reflects a pattern of water quality deterioration due to the numerous land use practices that have had an adverse impact on the receiving waters. In addition, the communities that use this water for various purposes have been impacted on. Spatially, an impoverishment of water quality conditions were observed by the increase of phosphorus, turbidity, DO and E.coli. The parameter of E.coli represented the worst water quality status in all the catchments of the study area. These were attributable to land use factors such as informal settlements and urban formal settlements. For the parameter of pH, no adverse water quality was present. Temporal evaluation of the data indicated that although there were no distinct trends that could be directly related to rainfall, some of the parameters showed some influence by rainfall, through runoff. Selected rivers of the Durban Metropolitan Area reflect poor water quality. There is an urgent need for the implementation of a strategy for improvement. The evaluation of water quality forms the basis for integrated catchment management (ICM), which has been advocated as the only holistic way to manage water resources. Due cognisance must be taken of the various land use practices, which have a great impact on water quality. There should be concerted efforts to reduce the input of pollutants that may have adverse effects on water quality, e.g., by provisions of sanitation facilities in informal settlements. It is hence recommended that for any improvement of water quality to occur, there needs to be an implementation of ICM for all the catchments. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2002.

Human activities such as urbanisation, sewage treatment, industrialisation and agriculture represent major human interference in water resources. The water resources are affected both quantitatively as well as qualitatively by these activities. The impact of human interference in the Mukuvisi River catchment hydrology was studied by determining the concentration values of eight selected physico - chemical and biological parameters. These are pH, temperature, total nitrates, total phosphates, Dissolved Oxygen, Biological Oxygen Demand, lead, copper and Total Faecal Coliform Count. Seven sites were sampled along the river, from up the river in Mukuvisi woodlands up to the point where the river discharges into Lake Chivero. Analysis of the results obtained was undertaken using SPSS (paired sample T test) and descriptive graphs were drawn using Microsoft Excel 2010. Nitrates, phosphates, copper, lead and Total Faecal Coliform Counts were found to be higher than the Zimbabwe National Water Authority (ZINWA) maximum and World Health Organisation, 2011 (WHO) permissible standards from site 3 to site 7. Site 6 recorded the highest concentrations of all the measured parameters, except for pH and Dissolved Oxygen. Mean DO and BOD concentrations were 2.53 mg/l and 40 mg/l respectively at site 6. Mean total nitrates were 17.5 mg/l at site 6 above the ZINWA and WHO threshold of 10 mg/l. Site 6 also recorded a mean total phosphate of 5.9 mg/l which was above the ZINWA and WHO threshold of 0.5 mg/l. Mean TFCC was 992.6 mpn100ml-1 higher than the threshold of nil according to ZINWA and WHO threshold. Site 3 recorded the mean DO and TFCC of 2.4 mg/l and 2.80 mpn100ml-1 respectively. Site 2 had the lowest mean TFCC concentrations of 2.80 mpn100ml-1, which did not differ significantly from the WHO and ZINWA threshold of nil at p<0.05. The quality of water in the river varied from site to site in direct relation to the intensity and type of human activities along the river course. Levels of all the water quality indicators increased after discharge from the Firle sewage treatment plant at site 6 with the exception of temperature and pH. Sewage effluents, agricultural runoff and industrial effluents were found to be responsible for the high nutrient levels and high metal concentrations in the river which in turn reduced DO levels and increased BOD / College of Agriculture and Environmental Sciences / M. Sc. (Environmental Science)

Background: Access to safe water and sanitation are universal need and basic human right, but the provision of quality water and improved sanitation remains a challenge in many African countries including Ethiopia. Objectives: The study investigated drinking water quality, sanitation-hygiene practices and the potential of Moringa stenopetala seed powder for the purification of water in Bale Zone, Southeast Ethiopia. Methodology: A community-based cross-sectional study was conducted among 422 randomly selected households in Robe and Ginnir Towns. Data were collected by interviewer-administered structured questionnaires from June 2012 to August 2013. An observation checklist was used to observe the sanitary condition of water sources. A total of 71 water samples were collected using sterile glass bottles in accordance with the standard method of American Public Health Association APHA. The physicochemical and bacteriological water quality analyses were done in Addis Ababa Environmental protection and Oromia water and Energy laboratories. The efficiency of Moringa stenopetala seed powder for removal of turbidity, hardness, and nitrate was evaluated. Data were analyzed SPSS Version 21.0 for the window. Descriptive analysis was done for appropriate variables. Logistic regression was used to identify the factors associated with under-five diarrhea. The results were presented using adjusted odds ratio and P-value of < 0.05 was used to declare significance association. Results: From the total sample, 401 respondents participated making a response rate of 95%. More than one third (37.9%) of the respondents were found to use pipe water. Two hundred and eighty (69.8%) of households wash storage containers before refilling and 325 (81%) of households were using separate containers for water storage. Two hundred seventy (67.3%) of the households had pit latrine. Prevalence of childhood diarrhea was found to be 50.1%. From the logistic regression model, those households having access to clean water source are 68% less likely to have under-five diarrhea, the households having clean storage of drinking water are 45% less likely to have under-five diarrhea in their home, and those households having poor latrine sanitation are 68% more likely to have under-five diarrhea in their home. Seed powder of 200mg/l Moringa stenopetala reduced the Nitrate concentration doses from 5.49mg/l to 8.18mg/l, a 75mg/l was reduced the turbidity from 4.49NTU to 1.07 NTU. A total hardness of 427 was reduced by 7.8% after treatment with powder seed of Moringa stenopetala. Conclusion: Prevalence of childhood diarrhea was high and it is associated with lack of access to a clean water source, poor sanitation of drinking water storage and latrine. Prevalence of open field defecation was remarkably high. The iron content of drinking water was above the range of World Health Organization standards. Moringa stenopetala seed powder has efficiency in the reduction of total and faecal coliform, turbidity, hardness and nitrate level in drinking water Recommendation: Health education on water handling, sanitation and low-cost effective water treatment methods like Using Moringa stenopetala seed should be practiced at the household level. / Environmental Sciences / Ph. D. (Environmental Science)

The Texas Stream Team (TST) is one of an increasing number of citizen science water monitoring programs throughout the US which have been continuously collecting surface water quality data under quality assurance protocols for decades. Volunteer monitoring efforts have generated monitoring datasets that are long-term, continuous, and cover a large geographic area - characteristics shown to be valuable for scientists and professional agencies. However, citizen science data has been of limited use to researchers due to concerns about the accuracy of data collected by volunteers, and the decades of water quality monitoring data collected by TST volunteers is not widely used, if at all. A growing body of studies have attempted to address accuracy concerns by comparing volunteer data to professional data, but this has rarely been done with large-scale, existing datasets like those collected by TST. This study assesses the accuracy of the volunteer water quality data collected across the state of Texas by the TST citizen science program between 1992-2017 by comparing it to professional data from corresponding stations during the same time period, as well as comparing existing and experimental data from a local TST partner agency. The results indicate that even large-scale, existing volunteer and professional data with unpaired samples that may have been taken months apart can show statewide agreement of 80% for all parameters (DO = 77%, pH = 79%, conductivity = 85%) over the 38 years of sampling included in the analyses, across all locations. The local case study using paired datasets for which a greater number of factors were controlled for show an even higher agreement between volunteers and professionals (DO = 91%, pH = 87%, conductivity = 100%) and show no significant difference between experimental and existing sampling data. The results from this study indicate that TST has been collecting water sampling data for decades that is comparable to professional data and provides compelling evidence for researchers and managers to better utilize TST's and other large-scale monitoring datasets already in existence from similar citizen science water monitoring programs across the country.

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Civil Engineering / Master / Master of Philosophy

The purpose of this study was to examine the suitability of the YSI model 6600 Environmental Monitoring System (multiprobe) for long term deployment at a site in Lewisville Lake, Texas. Specifically, agreement between a laboratory extraction procedure and the multiprobe chlorophyll/fluorescence readings was examined. Preliminary studies involved determining the best method for disrupting algal cells prior to analysis and examining the precision and linearity of the acetone extraction procedure. Cell disruption by mortar and pestle grinding was preferable to bath sonication. Comparison of the chlorophyll/fluorescence readings from the multiprobe and the extraction procedure indicated that they were significantly correlated but temperature dependent.