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1
Filho, Antonio Alves de Oliveira. "Quality modeling of Poti River water." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13435.
Повний текст джерелаАнотація:
nÃo hÃThe disorderly growth of the capital of PiauÃ, marked mainly by housing occupancy on the banks of river Poti and the existence of clandestine connections of raw sewage in rainwater drainage pipes, has contributed significantly to the pollution of the waters of the river basin of the ParnaÃba River (semiarid region Brazil). This research consists of making water quality measuring campaigns in Poti river and sewage released that, via gallery rainwater, focusing on a river stretch of 36.8 km long, located in the city of Teresina / PI, as well as mathematical modeling of water quality of the river based on WHAT-UFMG platform. The research is presented as the first study involving modeling of water quality in a water body of the state of PiauÃ. Modeled components were: dissolved oxygen (DO), biochemical oxygen demand (BOD) and thermotolerant coliform (TC). The results of field measurements indicated TC parameter discontinuities with respect to CONAMA Resolution n 357/2005. The calibration of the decay coefficients for each parameter resulting in deviations between measured and modeled data of up to 20%, which shows that the QUALUFMG can be used as a basis for predicting the quality of water in rivers located in semiarid regions. The calibrated model was also compared to field data from the literature. Finally, simulations were performed for different flow scenarios (Q10, Q90 and Q7,10), with consistent results and that can be used for the management of water resources in the state of PiauÃ.
O crescimento desordenado da capital piauiense, marcado sobretudo pela ocupaÃÃo habitacional Ãs margens do rio Poti e pela existÃncia de ligaÃÃes clandestinas de esgoto bruto nas tubulaÃÃes de drenagem pluvial, tem contribuÃdo significativamente para a poluiÃÃo das Ãguas da bacia hidrogrÃfica do rio ParnaÃba (regiÃo semiÃrida do Brasil). A presente pesquisa consiste na realizaÃÃo de campanhas de mediÃÃo da qualidade da Ãgua no rio Poti e dos esgotos lanÃados no mesmo, via galeria de Ãguas pluviais, com foco em um trecho do rio de 36,8 km de extensÃo, localizado na cidade de Teresina/PI, bem como na modelagem matemÃtica da qualidade da Ãgua deste rio com base na plataforma QUAL-UFMG. A pesquisa apresenta-se como o primeiro estudo envolvendo modelagem da qualidade da Ãgua em um corpo hÃdrico do estado do PiauÃ. Os componentes modelados foram: oxigÃnio dissolvido (OD), demanda bioquÃmica de oxigÃnio (DBO) e coliformes termotolerantes (CT). Os resultados das mediÃÃes de campo indicaram desconformidades do parÃmetro CT com relaÃÃo à ResoluÃÃo CONAMA n 357/2005. A calibraÃÃo dos coeficientes de decaimento para cada parÃmetro resultou em desvios entre dados medidos e modelados de atà 20%, o que mostra que o QUAL-UFMG pode ser utilizado como base para prediÃÃo da qualidade da Ãgua em rios localizados em regiÃes semiÃridas. O modelo calibrado tambÃm foi comparado a dados de campo obtidos na literatura. Finalmente, foram realizadas simulaÃÃes para diferentes cenÃrios de vazÃo (Q10, Q90 e Q7,10), apresentando resultados coerentes e que podem ser utilizados para a gestÃo dos recursos hÃdricos do estado do PiauÃ.
2
Griffiths, Ian Martin. "Automatic river quality monitoring." Thesis, Brunel University, 1991. http://bura.brunel.ac.uk/handle/2438/7870.
Повний текст джерелаАнотація:
Automatic river quality monitoring (ARQM) is potentially an important tool in water quality management for the National Rivers Authority (NRA) and similar organisations worldwide. The information produced by ARQM systems must be used in the most effective way and fully integrated with the manual monitoring effort. The status and development of ARQM systems in the freshwater and estuarine River Thames catchment are discussed and a practical appraisal of the design, operation and maintenance requirements given. Data capture, verification and presentation methods are developed and the use of ARQM data for real time management and subsequent analysis is advocated. Examples of data from the freshwater ARQM system are given which emphasise the variability of freshwater quality and the need for a comprehensive understanding of the behaviour of rivers before management decisions are made. The use of ARQM data for assessing the compliance of rivers with River Quality Objectives is examined. With respect to the tidal Thames, data processing methods to correct for the tidal movement of the waterbody are developed. ARQM data are used to highlight the principal factors affecting the water quality of the tidal Thames. The importance of the use of ARQM information in the effective management of the tidal Thames is discussed and operational examples demonstrate how it may be utilised as a basis for management decisions. The application of ARQM to the sub-tropical environment of the River Ganges, India, is investigated. An ARQM system has been designed and prototypes are operational. Extensive site surveys were carried out and the water quality status of the Ganges is discussed. Recommendations for the improvement and future development of ARQM systems are made. The use of ARQM information and its potential for improving the management of rivers is discussed.
3
Norreys, Richard. "Water quality river impact model (RIM) for river basin management." Thesis, University of Salford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305863.
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4
Cheung, Sheung-ching. "Transboundary water pollution between Hong Kong and the Pearl River Delta Region : Dongjiang River as a case study /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25247645.
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5
Berger, Christopher John. "Water Quality Modeling of the Tualitin River." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4742.
Повний текст джерелаАнотація:
Water quality problems related to excessive algal growth, high nutrient loading, and low flows have been occurring along Oregon's Tualatin River. The Tualatin River is 86 miles long and has a drainage basin of 711 square miles. The drainage basin incorporates forest, agricultural, and urban areas. Located in the Portland metropolitan area, these problems have been acerbated by the effects of urban growth. To help analyze pollution control alternatives, a river model study, funded by the Oregon Department of Environmental Quality (DEQ}, was undertaken. An in-stream model of hydraulics and water quality was developed. The Corps of Engineer's CE-QUAL-W2 model, a twodimensional, laterally averaged, dynamic model of hydrodynamics and water quality was applied to the Tualatin system. Calibration of the main pool model of the Tualatin River was from field data taken during June through August of 1991. Verification of the model was performed from field data taken during the summer of 1990. After calibration and verification of the model, management alternatives were evaluated in order to achieve DEQ mandated water quality standards. Environmental performance criteria were determined to evaluate differences between model scenarios. Management alternatives focused on the reduction of point and non-point sources of pollution, flow augmentation, and structural changes in the river system, such as removal of the Lake Oswego Diversion Dam.
6
Cheng, Man-shun. "A review of river water quality in Hong Kong /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20042176.
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7
Lindenschmidt, Karl-Erich. "River water quality modelling for river basin and water resources management with a focus on the Saale River, Germany." [Potsdam] : [Bibliothek des Wissenschaftsparks Albert Einstein], 2006. http://deposit.d-nb.de/cgi-bin/dokserv?idn=981609600.
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8
McIntyre, Neil Robert. "Analysis of uncertainty in river water quality modelling." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11828.
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9
Sincock, Andrew Michael. "Conceptual river water quality modelling under dynamic conditions." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/11912.
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10
Spanou, Maria N. "Object-oriented programming in river water quality control." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10119.
Повний текст джерелаАнотація:
In the present work object-oriented analysis and design have been applied for the quality management of river systems. The physical entities of the river system, as well as the conceptual entities for the flow and water quality analysis, the simulation and the pollution control strategies, have been represented through objects. By distributing appropriate responsibilities to these objects, daily low flows of the river gauge stations can be estimated for a duration of seven and thirty days and a recurrence interval of twenty years. Based on these flows optimum windows for the whole system and minimum daily flows of the river gauge stations can be also estimated. The minimum daily flows of each point of the river system can be provided. These are the design river flows for the management study. A simple statistical analysis of water quality in the river and the effluent of the plants can be also performed and the background concentrations of major constituents can be estimated. The simulation of water quality in the system can be also performed based on the design conditions of the study. Control strategies for the improvement of water quality can be finally applied and an optimized discharge scheme can be suggested. (Abstract shortened by UMI.)
11
Zhu, Yan Carleton University Dissertation Geography. "Water quality of the South Nation River, Rideau River and Mississippi River; a statistical survey." Ottawa, 1993.
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12
Kamish, Wageed. "Hydrosalinity modelling of the Berg River using ACRUSalanity /." Link to the online version, 2008. http://hdl.handle.net/10019/2022.
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13
Hyde, Michael L. "Urban runoff quality in the River Sowe catchment." Thesis, Coventry University, 2006. http://curve.coventry.ac.uk/open/items/9c34fa51-7611-4362-844d-30d87ba84205/1.
Повний текст джерелаАнотація:
There have been no previous studies carried out on the impact of urban runoff in the Coventry City centre area. The culverted nature of the River Sherbourne, and many of its tributaries, makes the investigation of intermittent pollution and rainfall events expensive and impractical, when using traditional spot sample methods. Storm events have been monitored over a period of over 60 months upstream and downstream of the City, using continuous water quality monitors and auto-spot sample methods. The receiving waters of the River Avon had previously suffered annual fish mortalities as a result of summer storm events causing oxygen depletion. Previous studies (Clifforde and Williams 1997) on the impact of Coventry Sewage Treatment Works effluent on the watercourse, have suggested a major component of the intermittent pollutant load arising from the City (upstream of the Sewage Treatment Works), which requires evaluation and remediation. This research identifies the contaminants found during a series of storm events impacting on the River Sherbourne culvert, and discusses the relationship between them and the increased flow measured. The methodology was divided into 3 Phases; Phase 1 examined all of the watercourses in the River Sowe catchment, and identified the culverted streams and drainage system giving an indication of the presence of pollutant sources. Continuous monitors were deployed within the four identified drainage systems to pinpoint intermittent and illegal contaminated discharges, and these discharges were subsequently redirected to the foul sewer or stopped. Phase 2 examined the quality of the River Sherbourne culvert upstream and downstream of the city centre, and demonstrated (using continuous monitors and automated sampling), that six combined sewer overflows discharging to the watercourse upstream of the culvert were opening unsatisfactorily. The dissolved oxygen levels were significantly reduced during rainfall events (with a loss of diurnal variation), and total ammonium levels exceeded current water quality standards. The results were used to instigate a remediation scheme to replace the overflows with additional foul sewage capacity, and a single high-level storm relief. Phase 3 examined the impact of urban runoff during rainfall events after the improvements made following Phases 1 and 2. The results suggest a marked improvement in the water quality, with little impact from organic pollutants. Dissolved oxygen concentrations remained high during many of the post-remedial rainfall events, and ammonia levels remained largely insignificant. The results indicated a fall in pH levels during the rainfall events and increases in all of the heavy metals analysed, though not beyond current water quality guidelines. The efficiency of using continuous monitoring in Coventry was assessed and likely sources of the contaminants in urban runoff were considered. The statistics of compliance with percentile standards do not allow for short-term pollution or storm events, which may kill all aquatic life whilst not breaching water quality standards. Using continuous monitors to identify intermittent and illegal discharges in underground drainage systems was an efficient and cost-effective method of reducing the impact of urban runoff in a failing watercourse. The methodology can be applied to other urban areas to identify unidentified illegal and intermittent point sources. Routine monthly monitoring of an urban watercourse may not identify the peaks and troughs associated with rainfall events that may breach toxicological guidelines, and will not identify intermittent and unknown pollutant sources; particularly when discharging outside of normal working hours. This research was a unique and comprehensive investigation into the nature and composition of urban runoff in the City of Coventry, and local data gathered will be invaluable in promoting further research, improving local knowledge of the urban environment in preparation for the Water Framework Directive (2000/60/EC), and in planning for environmental improvements in the future.
14
Defenbaugh, Angela Lynn. "Evaluating Ohio River Basin Waters: A Water Quality and Water Resources Internship with the Ohio River Valley Water Sanitation Commission." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1389295851.
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15
Adeloye, A. J. "Value of river flow data for water resources and water quality assessment." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378277.
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16
Boitsidis, Periklis. "Aspects of water quality management in an urban river : the Upper River Tame." Thesis, University of Birmingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404172.
Повний текст джерелаАнотація:
This research addresses the mechanistic behaviour of urban catchments, particularly under the effects of rainfall, using the case study of the River Tame in West Midlands. The catchment response in wet weather is dominated by combined sewer overflow (CSO) discharges into the river, causing water quality deterioration. Severe dissolved oxygen depletions often occur resulting in exceedence of fundamental intermittent standards; high ammonia values are also induced but un-ionised ammonia is constrained by the prevailing neutral pH. The five most dominant responses of ammonia to rainfall are identified based on antecedent dry period, the magnitude and timing of ammonia peaks, the initial rainfall intensity gradient and the patterns of rainfall intensity. Field studies support data analysis of the historic database and establish a temporal picture of BOD during storms. Simulation of river flow and water quality in low flow and storm conditions is achieved by employing MIKE 11, a deterministic mathematical model, with variable fidelity due to temporal and spatial variability of inputs. The model is tested as part of a decision support system for river water quality management. Simulations of various management schemes indicate that real time CSO control and on-line storage cannot guarantee water quality improvements and that on-line storage can be useful in combating excessively high pollutant concentrations.
17
Andrish, Sean David, and Sean David Andrish. "Water quality management in the Quinnipiac River Basin, Connecticut." Thesis, The University of Arizona, 1997. http://hdl.handle.net/10150/626903.
Повний текст джерелаАнотація:
The Quinnipiac River Basin, located in South-Central Connecticut, flows 38 miles
from its headwaters in New Britain and Plainville, Connecticut to its mouth in New
Haven Harbor. The basin is heavily developed, with the majority of the land in the basin
classified as residential and urban. The five municipal water pollution control facilities
and one large privately-owned water pollution control facility operating in the Quinnipiac
Basin are responsible for maintaining acceptable levels of water quality in the
Quinnipiac River.
While the current water quality management practices within the Quinnipiac
River basin are capable of meeting the various water quality standards, changes in the
water quality management system may require an adjustment or alteration of the current
management practices. The focus of this study is the identification of possible
improvements to the current water resources management practices in the Quinnipiac
Basin and a discussion of the feasibility of the proposed improvements.
18
Cheng, Man-shun, and 鄭文順. "A review of river water quality in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31253982.
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19
Luo, Ying. "Integrated assessment of water quality at river basins context." Thesis, University of Newcastle Upon Tyne, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435626.
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20
Awang, Suriyani. "A water quality study of the Selangor River, Malaysia." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/67083/.
Повний текст джерелаАнотація:
Malaysia’s rapid economic and demographic development has put pressures on its water supplies and consequently on the quality of its river water. The Selangor River, close to the nation’s capital, is now a major source of water and there are fears that its water quality will deteriorate. The Malaysian Government in its Vision for Water 2025 states that rivers should achieve Class II as measured by Malaysia’s Water Quality Index (WQI) (Class I is cleanest). The objectives of this thesis are to investigate the effects of flow through the 10 major tidal control gates (TCGs) which regulate run-off from the oil-palm plantations into the river, and to predict the water quality for the river in 2015, 2020 and 2030. In order to achieve these objectives it was necessary to set-up, calibrate and validate a commercial one-dimensional numerical model, InfoWorks, which includes both the hydrodynamics and water quality of the river-estuary network. It was concluded that there was insufficient hydrodynamic (stage and current) and water quality data to fully calibrate and validate the InfoWorks model but it performed well when compared with measured salinity transects. The model was found to be relatively insensitive to the choice of diffusion parameters but needed a high value for the oxygen transfer velocity, 0.3 m h-1, to get reasonable values for the dissolved oxygen (DO) along the river. The effect of run-off through the TCGs was less than expected and attributed to the high oxygen transfer velocity and needs to be addressed before the model can properly represent run-off through the TCGs. The model shows the WQI of the lower reaches of the river to be Class III in both wet and dry seasons except close to the estuary where it is Class II due to tidal flushing. The dissertation identifies several deficiencies in the model; the lack of an operational ramp function at the estuary boundary, the use of a single value of the oxygen transfer velocity throughout, and the exclusion of water extraction. Land-use changes above Rantau Panjang, the upper boundary of the InfoWorks model, and water quality data were used to estimate the water quality and its uncertainties at Rantau Panjang in 2015, 2020 and 2030 due to predicted development in the upper catchment for both wet and dry seasons. InfoWorks models of water quality along the river in 2015, 2020 and 2030, which included extraction at the Batang Berjuntai barrage, predict little change in the WQ (Class II/III boundary) below the barrage during the dry season but a rapid deterioration in the wet season (down to Class III/IV by 2030) showing the importance of water extraction to the water quality of the river. Overall, because of its relative simplicity and ease of operation, InfoWorks is considered to be a useful tool for river management in Malaysia.
21
Patroncini, Deborah. "Water quality investigations of the River Lea (NE London)." Thesis, University of Bedfordshire, 2013. http://hdl.handle.net/10547/303790.
Повний текст джерелаАнотація:
The Lea Navigation in the north-east of London, a canalised reach of the River Lea, is affected by episodes of very low levels of dissolved oxygen. The problem was detected by the Environment Agency in the stretch from the confluence with Pymmes Brook (which receives the final effluent of Deephams sewage treatment works) to the Olympic area (Marshgate Lane, Stratford). In this project, possible causes and sources of the poor water quality in the Lea Navigation have been investigated using a multi-parameter approach. A study of physico-chemical parameters, obtained from Environment Agency automated monitoring stations, gave a clear picture of the poor river water quality at three sites in this reach. River water ecotoxicity to the freshwater alga Pseudokirchneriella subcapitata was determined by algal growth inhibition tests, following the OECD guidelines. Moreover, a novel protocol was developed which involved the use of E. coli biosensors (CellSense) operating at a lower potential than the standard protocol and using pre-concentrated river water samples. This protocol is promising and it has the potential to be a useful tool to determine the toxicity of contaminants at environmental concentrations. Furthermore, the developed protocol is a rapid, easy to perform bioassay, with potential application in achieving the aims of the Water Framework Directive (WFD). In addition to the data from the Environment Agency automatic monitoring stations and the laboratory-based tests, two in situ monitoring approaches were performed: 1) a detailed spatial seasonal monitoring of physico-chemical parameters of river water at twenty-three sites, and 2) algal growth inhibition tests, with algae entrapped in alginate beads, at seven monitoring stations. Results showed chronic pollution, and identified polar compounds in the river water and high bacterial concentrations as possible causes of low dissolved oxygen levels. This study confirmed the negative impact of Deephams STW (throughout Pymmes Brook) on the water quality of the Lea Navigation. However, there was evidence of other sources of pollution, in particular Stonebridge Brook was identified as uncontrolled source of pollution and untreated wastewater. Other possible sources include Old Moselle Brook, diffuse pollution from surface runoff, boat discharges and other undetected misconnections. Finally, in the light of the WFD, this project provides a case study on the investigation of river water quality, providing evidence that the multiparameter approach is reliable, and low cost approach for the monitoring of freshwater bodies.
22
Maeda, Shigeya. "Optimization of wasteload allocation for river water quality management." Kyoto University, 2002. http://hdl.handle.net/2433/78139.
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Kyoto University (京都大学)0048
新制・課程博士
博士(農学)
甲第9623号
農博第1251号
新制||農||843(附属図書館)
学位論文||H14||N3655(農学部図書室)
UT51-2002-G381
京都大学大学院農学研究科地域環境科学専攻
(主査)教授 河地 利彦, 教授 青山 咸康, 教授 高橋 強
学位規則第4条第1項該当
23
Thoe, Wai. "Integrated river management of the East River field studies, hydrologic and water quality modelling /." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38997575.
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24
Goodhue, Nigel David. "Hydrodynamic and water quality modelling of the lower Kaituna River and Maketu Estuary /." The University of Waikato, 2007. http://hdl.handle.net/10289/2375.
Повний текст джерелаАнотація:
The Maketu Estuary is a shallow intertidal estuary (2.3 km2) located in the Bay of Plenty, North Island, New Zealand. The Kaituna River contributes the largest freshwater flow into the estuary through control gates. Lake Rotoiti and indirectly Lake Rotorua supply the base flow to the Kaituna River, with tributaries along the 50 km reach also significantly contributing to the flow. Water quality within the river is affected by elevated nutrients, faecal coliforms, high oxygen demand and algae concentrations derived from the lakes as well as contributions from tributaries and industrial and urban discharge. Through the use of a coupled hydrodynamic-biogeochemical numerical model ELCOM-CAEDYM, this study aims to examine the nutrient, phytoplankton and hydrodynamics of the Maketu Estuary and lower Kaituna River. Water quality and hydrodynamic measurements were sourced from Environment Bay of Plenty's data archives as well as a number of instrument deployments to collect water velocity, tidal elevation and salinity and temperature measurements during the course of this study. Included in the field work was a survey of the lower river and estuary bathymetry. Model simulations predicted that the maximum residence time in the Maketu Estuary is 1.5 days, occurring in the inner western region. Residence time in the lower river (mouth to 8.5 km upstream) is in the order of hours although some variations were predicted near the river mouth. Growth rates of four phytoplankton groups where assessed over a 15 day period in January 2004. In the Kaituna River ELCOM-CAEDYM predicted that the community growth rates were small with the exception of a slight increase in biomass of the two freshwater groups in a semi-detached river bend. The increase in the loop was correlated with an increase of residence time. In the estuary, marine diatoms showed the highest growth rates in the western region which is expected to relate to retention time and available nutrients. Dinoflagellates showed the smallest variation in predicted growth rates, most likely due to their broad salinity tolerance. The two freshwater species showed a reduction in abundance when mixed with marine water. A principle limiting factor to phytoplankton growth in both the river and estuary is the low residence time. A number of scenarios were simulated in the river and estuary by altering the forcing conditions in the model. A simulation of the increased nutrient load associated with the Rotoiti diversion wall revealed that phytoplankton growth in the river and estuary will not be significantly affected. Because of the close proximity of the control gates to the river mouth, a proportion of water drawn through the structure can be marine. By opening the old river channel, model simulations predicted that a reduction in salinity would be possible, however the outcome of complete freshwater is probably not achievable. Increasing the discharge volume from the river into the estuary was also simulated. The results indicated that increasing the freshwater inflow at Fords Cut would reduce the salinity in the estuary while increasing the net (residual) flow towards the estuary mouth. Increasing the flow would also result in a greater range of salinity in regions of the estuary. Changing the inflow location to the historic Papahikahawai Channel also affected the salinity in the estuary. The most significant effect of an inflow at this location was a reduction of the residual currents in the western region of the estuary.
25
Sham, Wing-cheong. "The determination of mercury in sediment, river water and seawater samples, and the determination of Cr(VI) in river water /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13193739.
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26
Scott, Casey G. "Biological water quality assessment of the Little Wolf River watershed /." Link to full text, 2007. http://epapers.uwsp.edu/thesis/2007/scott.pdf.
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27
Lindenschmidt, Karl-Erich [Verfasser]. "River water quality modelling for river basin and water resources management with a focus on the Saale River, Germany / Karl-Erich Lindenschmidt." [Potsdam] : [Bibliothek des Wissenschaftsparks Albert Einstein], 2006. http://d-nb.info/981609600/34.
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28
Thoe, Wai, and 陶煒. "Integrated river management of the East River: field studies, hydrologic and water quality modelling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38997575.
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29
Weiss, Steven P. "Bioassessment of the West Branch of the Wolf River /." Link to full text, 2007. http://epapers.uwsp.edu/thesis/2007/weiss.pdf.
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30
岑永昌 and Wing-cheong Sham. "The determination of mercury in sediment, river water and seawater samples, and the determination of Cr(VI) in river water." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210533.
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31
Reda, Luiz de Lima. "Simulation and control of stormwater impacts on river water quality." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338878.
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32
Duong, Thi Thuy, Thi Phuong Quynh Le, Tu Cuong Ho, Thi Nguyet Vu, Thi Thu Hang Hoang, Dinh Kim Dang, and Xixi Lu. "Phytoplankton community structure and water quality of Red River, Vietnam." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-176893.
Повний текст джерелаАнотація:
This study aimed to describe the distribution and relative abundance of the phytoplankton obtained during the two seasons (rainy and dry seasons) from the Red river system. The water and phytoplankton samples were monthly collected during the year 2012 at four sampling stations along the Red River (Yen Bai, Vu Quang Hoa Binh,and Ha Noi) . Environmental variables (e.g. temperature, dissolved oxygen, pH, suspended solids, conductivity, TDS, NO3-N, NH4-N, PO4-P, Total Phosphorus; and DOC) and phytoplankton (e.g. cell density and relative abundant species) were analyzed. Six phytoplankton classes were identified with the Bacillariophyceae dominating in the phytoplankton community. A distinct seasonal variation in phytoplankton structure was observed with high cells density in dry season and low values in rainy season. PCA (Principal Component Analysis) showed that suspended solid factor that governed the temporal and spatial distribution of phytoplankton structure in the Red River systemNghiên cứu này trình bày sự phân bố và độ phong phú tương đối của quần xã thực vật nổi vào mùa mưa và mùa khô trong hệ thống sông Hồng. Các mẫu nước và thực vật nổi được thu hàng tháng trong năm 2012 tại 4 điểm trên sông Hồng (Yên Bái, Vụ Quang, Hòa Bình và Hà Nội). Các thông số môi trường (nhiệt độ, lượng oxy hòa tan, pH, chất rắn lơ lửng, độ dẫn, TDS, NO3-N, NH4-N, PO4-P, T-P và DOC) và thực vật phù du (mật độ tế bào,độ phong phú tương đối ) đã được phân tích. Sáu lớph tảo được được xác định với tảo silíc chiếm ưu thế trong quần xã thực vật phù du. Sinh khối thực vật đạt giá trị cao vào mùa khô trong khi thấp vào mùa mưa. Phân tích hợp phần chính cho thấy yếu tố chất rắn lơ lửng đóng vai trò quan trọng việc xác định biến động thời gian và không gian cấu trúc quần xã thực vật nổi trong hệ thống sông Hồng
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Momoh, Jinnah Samuel. "Decision support system for river water quality forecasting and management." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246651.
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Heng, Hannah Yik Siew. "Water quality and habitat modelling in the urban River Tame." Thesis, University of Birmingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398888.
Повний текст джерелаАнотація:
Urban river research is scarce. This thesis improves aspects of water quality and physical habitat modelling for sustainable urban river environment management using the highly urbanised River Tame in the West Midlands, UK as study catchment. Specifically, it focuses on modelling at the catchment scale and during high flow discharges. The River Tame system response to rainfall precipitation is complex, with influences from the wastewater treatment acting concurrently with the intricate combined sewer system. The complexity of the system response results in disassociation of critical flows with critical water quality such as biological oxygen demand and ammonium, reinforcing the belief that a design event approach for wet weather flow water quality modelling is impractical. Modelling intermittent discharges on a catchment scale basis through utilisation of a simple water quality model as opposed to sophisticated commercial tools achieved an acceptable level of success. The same order of high flow load magnitudes as field observations was found. Modelling physical habitat availability using a catchment scale methodology, which eliminates the need for intensive field data collection was also demonstrated, suitable for use particularly when water quality and flow regimes are the overriding factors in urban river health. Here, habitat suitability observations from high-resolution simulations over short distances were found to replicate lower resolution simulations over longer distances where cross-sectional shape and channel slope of study reaches are similar. However, with the knowledge of inter-relationships between the flow, water quality and physical habitat remains limited, the development of an integrated management between these different fields is still immature.
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Klotz, Jason, and Aregai Tecle. "RESTORING THE WATER QUALITY OF THE SAN PEDRO RIVER WATERSHED." Arizona-Nevada Academy of Science, 2015. http://hdl.handle.net/10150/621703.
Повний текст джерелаАнотація:
This paper is concerned with restoring the quality of water in some portions of the San Pedro River. There are high concentrations of bacteria in some parts of the San Pedro River. Our aim is to find ways of improving the situation. Specifically, there are two objectives in the study. The first one attempts to identify the possible sources of the bacterial contamination and assess its trends within the watershed. The second objective is to determine appropriate methods of restoring the water quality. The main water quality problem is nonpoint source pollution, which enters the stream and moves along with it. The magnitude of the problem is affected by the size and duration of the streamflow, which brings bacteria-laden sediment. The amount of sediment brought into the system is large during the monsoonal events. At this time, the streamflow becomes highly turbid in response to the organic and inorganic sediments entering the system. Based on research done for this paper, the amount of bacterial concentration is strongly related to turbidity. Best management practices (BMPs) have been designed and implemented to restore the water quality problem in the area. The BMP's consist of actions such as monitoring, educational outreach, proper signage, and other range/watershed related improvement practices. Other issues that contribute to the increasing amount of bacteria that are briefly addressed in this paper are bank and gully erosion, flood control, and surface water and streamflow issues that occur on the stream headwaters.
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Duong, Thi Thuy, Thi Phuong Quynh Le, Tu Cuong Ho, Thi Nguyet Vu, Thi Thu Hang Hoang, Dinh Kim Dang, and Xixi Lu. "Phytoplankton community structure and water quality of Red River, Vietnam." Technische Universität Dresden, 2014. https://tud.qucosa.de/id/qucosa%3A28881.
Повний текст джерелаАнотація:
This study aimed to describe the distribution and relative abundance of the phytoplankton obtained during the two seasons (rainy and dry seasons) from the Red river system. The water and phytoplankton samples were monthly collected during the year 2012 at four sampling stations along the Red River (Yen Bai, Vu Quang Hoa Binh,and Ha Noi) . Environmental variables (e.g. temperature, dissolved oxygen, pH, suspended solids, conductivity, TDS, NO3-N, NH4-N, PO4-P, Total Phosphorus; and DOC) and phytoplankton (e.g. cell density and relative abundant species) were analyzed. Six phytoplankton classes were identified with the Bacillariophyceae dominating in the phytoplankton community. A distinct seasonal variation in phytoplankton structure was observed with high cells density in dry season and low values in rainy season. PCA (Principal Component Analysis) showed that suspended solid factor that governed the temporal and spatial distribution of phytoplankton structure in the Red River system.Nghiên cứu này trình bày sự phân bố và độ phong phú tương đối của quần xã thực vật nổi vào mùa mưa và mùa khô trong hệ thống sông Hồng. Các mẫu nước và thực vật nổi được thu hàng tháng trong năm 2012 tại 4 điểm trên sông Hồng (Yên Bái, Vụ Quang, Hòa Bình và Hà Nội). Các thông số môi trường (nhiệt độ, lượng oxy hòa tan, pH, chất rắn lơ lửng, độ dẫn, TDS, NO3-N, NH4-N, PO4-P, T-P và DOC) và thực vật phù du (mật độ tế bào,độ phong phú tương đối ) đã được phân tích. Sáu lớph tảo được được xác định với tảo silíc chiếm ưu thế trong quần xã thực vật phù du. Sinh khối thực vật đạt giá trị cao vào mùa khô trong khi thấp vào mùa mưa. Phân tích hợp phần chính cho thấy yếu tố chất rắn lơ lửng đóng vai trò quan trọng việc xác định biến động thời gian và không gian cấu trúc quần xã thực vật nổi trong hệ thống sông Hồng.
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O'Donnell, Thomas Kevin. "River restoration in the upper Mississippi River Basin." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4532.
Повний текст джерелаАнотація:
Thesis (M.S.) University of Missouri-Columbia, 2006.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 27, 2007) Includes bibliographical references.
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Hess, Anna N. "The Wolf River and Rock River watersheds : developing a regional curve for bankfull stage /." Link to full text, 2009. http://epapers.uwsp.edu/thesis/2009/Hess.pdf.
Повний текст джерелаАнотація:
Thesis (M.S.)--University of Wisconsin--Stevens Point, 2009.Submitted in partial fulfillment of the requirements of the degree Master of Science in Natural Resources (Water Resources), College of Natural Resources. Includes bibliographical references (leaves 113-115).
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Latham, Zachary B. "Dissolved oxygen dynamics in the Carson River, Nevada." abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433406.
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Lindenschmidt, Karl-Erich [Verfasser], and Albrecht [Gutachter] Gnauck. "River water quality modelling for river basin and water resources management with a focus on the Saale River, Germany / Karl-Erich Lindenschmidt ; Gutachter: Albrecht Gnauck." Cottbus : BTU Cottbus - Senftenberg, 2018. http://d-nb.info/1177802813/34.
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Santy, Sneha. "Anthropogenic Influence on River Water Quality." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6024.
Повний текст джерелаАнотація:
Anthropogenic factors such as climate change, land use land cover change and industrial and population growth can influence river water quality. Climate change affects water quality due to changes in stream temperature and streamflow due to increased air temperature and varied precipitation patterns associated with warming. Land use land cover influences water quality mainly from the agricultural runoff, which carries the pollutants from fertilizers and pesticides and reaches the nearby water body. Population growth can increase the water demand and sewage generated hence aggravating pollution. Industrial growth has the potential to affect water quality through increased effluent loads. The work presented in this thesis contributes to quantifying such anthropogenic influences on river water quality using a coupled hydrological-water quality simulation model. The study area considered is a 238km stretch of Ganga river in India from Ankinghat to Shahzadpur, passing through Kanpur, which is identified as the most polluted stretch of Ganga river by the Central Pollution Control Board of India.
Sensitivity studies with forcings such as climate change and land use are extremely important for any management decision on water quality. In the initial part of the thesis, the sensitivity of nine water quality parameters to climate change and land use change is assessed using idealized scenarios and a standalone water quality simulation model, QUAL2K. The key input model parameters contributing to model uncertainty and key locations are identified using first order reliability analysis. The water quality parameters considered are DO, BOD, ammonia, nitrate, total nitrogen, organic-, inorganic-, and total phosphorous and faecal coliform. The non-point source pollution is quantified using the export coefficient method, in which pollutants from all land use classes are considered. Eight climate change and six land use land cover scenarios are framed based on historical data analysis to assess their sensitivity to water quality parameters. DO is the most sensitive indicator to the climate change scenarios considered, while nutrients and faecal coliform are more sensitive to the land use scenarios. In general, the water quality parameters are found to improve with a rise in air temperature and deteriorate with a reduction in streamflow. An increase in the agricultural land area leads to higher nutrient concentration, while an increase in the built-up area causes an increase in faecal coliform concentration. An increase in forest land shows better water quality in terms of all water quality parameters. The key input variables contributing to the uncertainty of water quality simulation are the head water discharge, point and non-point pollution loadings, water temperature, and corresponding reaction rates. The key locations identified using first order reliability analysis are Kanpur downstream and Jajmau downstream.
Risk assessment studies on water quality for future scenarios are limited in the literature. In the next part of the thesis, the effect of climate change on water quality, the risk of eutrophication and fish kill for the mid-and end of the 21st century for this river stretch are assessed. The risk of eutrophication and fish kill are quantified using simulated concentrations of nutrients and DO, respectively. Downscaled climate change projections for two climate change scenarios (RCP4.5 and RCP8.5) are used to drive a hydrological model coupled with a water quality simulation model. The simulations indicate a potential deterioration of water quality in this stretch in the mid-21st century, with a potential increase in pollutant concentration by more than 50% due to climate change alone. The risk of reduced dissolved oxygen and increased organic and nutrient pollution, and the risk of eutrophication and fish kills increase with warming due to the rise in the frequency of low-flow events and a reduction in streamflow during low-flow events. However, the risk of nitrate and microbial pollution is reduced due to increased denitrification and pathogen decay rates with warming. The risk of eutrophication and fish kill is found to increase by 43.5% and 15% due to climate change alone by the mid-21st century. The risk of eutrophication is found to increase by 6% due to land use change which can be attributed to an increase in nutrient loading with land use change.
In the final part of the thesis, the individual effects of climate change, land use land cover change, population and industrial growth on river water quality are assessed with a coupled hydrological-water quality simulation model and the predominant factor contributing to pollution is identified. Also, the future water quality is projected for mid 21st century considering climate change, land use projections, population and industrial growth, and the proposed treatment for the stretch considered using socio-environmental scenarios. The effectiveness of the proposed treatment to offset the reduction in water quality from anthropogenic forcings is also assessed. The climate change effect is found to have a larger effect on water quality than other drivers, with a percentage contribution of above 70% because of the considerable sensitivity of water quality parameters to the amount of streamflow. Climate change projections combined with socio-environmental scenarios imply that the large increase in pollution due to climate change, land use land cover, industry, and population growth cannot be controlled by the current treatment proposals for 2050 by the authorities. However, providing adequate STPs to meet the population of 2050, and allowing only domestic sewage to reach STPs can help in achieving the objective of the Ganga Action Plan in the mid-21st century.
The thesis comprises of five chapters. An introduction to the problem addressed, and the objectives of the work presented in the thesis are provided in Chapter 1. Details of the case study and analysis of the sensitivity of water quality parameters to climate change and land use with idealized future scenarios are discussed in Chapter 2. In Chapter 3, the risk assessment of low water quality, eutrophication and fish kill under changing climate and land use land cover is presented. Chapter 4 presents the analysis of the individual effects of all external forcings, including climate change, land use change, population and industrial growth. Conclusions drawn from the study are presented in Chapter 5.
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TIWARI, ABHIJEET. "SPATIO- TEMPORAL WATER QUALITY ASSESSMENT OF RIVER GANGA AT DIFFERENT LOCATIONS IN WEST BENGAL, INDIA THROUGH WQI AND SPI." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20078.
Повний текст джерелаАнотація:
An effort has been develop to access the water quality status of river Ganga in West Bengal India for drinking purpose using unified techniques. For this study, 14 parameters at 10 location from Beharampur to Diamond Harbour over 39 months (2020january —2023march) were considered. The eastern stretch of Ganga showed a variation of Water Quality Index (WQI) from 24.4539 to 1790.2545 and Synthetic Pollution Index (SPI) from 0.244539 to 1.7902545 in 36 months. . The map interpolated through GIS exposed that the entire river stretch in 36 months and location near to ocean during the entire period of 36 months were severely polluted (WQI >100 or SPI > 1).Turbidity ,DO and BOD concentration mainly contribute to the high scores of indices. Further, the origin of these ions was estimated through multivariate statistical techniques using SPSS .. It was recognized that the origin of these pollutant is mainly attributed to seawater influx, that of fluoride to human and industrial activities , and other parameters originated through geological as well as human activities. Based on the research, a few possible water treatment mechanisms are suggested to render the water fit for drinking.
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Chang, Tso-Chen, and 張祚楨. "Evaluation on Water Quality Index for River Water Quality Management." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/02708301062910863950.
Повний текст джерелаАнотація:
碩士淡江大學
水資源及環境工程學系碩士班
101
The water quality index (WQI) can be applied to river water quality management. The Taiwan EPA uses river pollution index (RPI) to evaluate the performance of river pollution control. The RPI’s water quality parameter includes SS, BOD, DO and NH3-N. The purposes of this study were: (1) to investigate the correlation between RPI and water quality parameters, (2) to examine the correlation between RPI and WQI, and (3) to evaluate the replacement of RPI with WQI7 in river water quality management. The data of river water quality, which was monitored during 2007-2011, was collected from Taiwan EPA. The results show that RPI value increased with increasing both concentration of BOD and NH3-N, and the correlation coefficient values (R2) between RPI value and BOD, NH3-N ranged from 0.82 to 0.86. By contrast, the R2 value of 0.04 was very low between RPI and SS, implying that RPI was unsuitable for SS concentration higher than 100 mg/L. WQI4 has the same water quality parameters with RPI, while WQI7 has three additional parameters as pH, total phosphorus (TP) and coliform groups. Both R2 values between RPI and WQI4, WQI7 are 0.91, and statistics F-value test shows that both WQI4 and WQI7 could replace RPI. The amount of 48.6% of river water quality could be classified as excellent and good grade by WQI4 and it became lower as 41.4% by WQI7. This is due to water quality parameters of WQI4 less than that of WQI7. In addition, this study calculated the change of RPI and WQI7 values at Tamsui River Basin’s monitoring stations water quality between 2007 and 2011 to evaluate the improvement ratio of river water quality. The results show the improvement ratio by RPI value was 50-55% higher than that of 10-20% by WQI7 value. This is because that the value range of RPI (1-10) is smaller than that of WQI7 (0-100). It implies that WQI7 was more suitable than RPI for evaluating river water quality improvement ratio. Moreover, WQI7 is based on the surface classification and water quality standards. Therefore, it could conclude that EPA can replace RPI with WQI7 to manage river water quality and evaluate improvement ratio of river water quality.
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Fonseca, André Ribeiro da. "River Water Quality Modelling for River Basin and Water Resources Management." Doctoral thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/77763.
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Fonseca, André Ribeiro da. "River Water Quality Modelling for River Basin and Water Resources Management." Tese, 2014. https://repositorio-aberto.up.pt/handle/10216/77763.
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Berger, Christopher J. "Water quality modeling of the Tualatin River /." 1994. http://hdl.handle.net/1957/12718.
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TUAN, DO HUU, and 杜有俊. "Network Design for River Water Quality Monitoring." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/53822404477607614732.
Повний текст джерелаАнотація:
博士國立臺灣大學
環境工程學研究所
100
River water quality monitoring data are essential to water basin development and decision making. The location of sampling stations is one of the most critical factors in monitoring network design. Most previous studies have focused on mathematical aspects of monitoring network design, whereas human activities within a river basin are often not considered comprehensively. Nutrient concentration is an important factor in identifying the quality of water sources and the likelihood of eutrophication. A nutrient monitoring network is an important information source that provides data on the nutrient pollution status of rivers. Export coefficient models have been widely used to study non-point source pollution. However, there has been little discussion about applying non-point source pollution and export coefficient modeling to design sampling points for monitoring. River water quality sampling frequency is an important aspect of the river water quality monitoring network. A suitable sampling frequency for each station as well as for the whole network will provide a measure of the real water quality status for the water quality managers as well as the decision makers. The Analytic Hierarchy Process (AHP) is an effective method for decision analysis and calculation of weighting factors based on multiple criteria to solve complicated problems. In this study, a new water monitoring network design procedure is introduced to identify representative river water quality sampling locations. The new procedure combines river mixing length, human activities, and geographic information systems (GIS) to locate position of sampling points. An implementation of a new potential pollution score (PPS) model of land use helps to classify the importance of each sampling point prior to selecting the most appropriate locations for an entire river system. The new procedure is applied on river system of Xindian City and Wulai Township. With the study area, urban and agriculture are mainly concentrated in the downstream area and were the primary non-point pollution sources in this region. Seven sampling points are proposed to monitor river water quality in the Xindian City and Wulai Township. In addition, a new procedure providing a comprehensive solution was proposed to design nutrient monitoring points, from identifying pollution sources to designing sampling points and frequencies. Application of this procedure to design nutrient monitoring points upstream from the Feitsui reservoirs, Taipei, Taiwan, indicated that agriculture occupied only 7.24% of the area, but it released 45,795 kg/yr, or 41%, of the total nutrient load from non-point sources. Additionally, the optimization conditions defined four sampling points as well as the frequency of sampling at those points in the upstream of the Feitsui reservoirs. Finally, this study introduces a new procedure to design river water quality sampling frequency by applying the AHP. We introduce and combine weighting factors of variables with the relative weights of stations to select the sampling frequency for each station, monthly and yearly. The new procedure was applied for Jingmei and Xindian river system. The results showed that sampling frequency should be increased at Po, Jhongjheng, Hua Zhong, and Hua Jiang stations. In addition, the number of samples in January, April, and October should be increased relative to the other months.
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Shaik, Rehana. "Uncertainty Modeling For River Water Quality Control." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/848.
Повний текст джерелаАнотація:
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.
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Shaik, Rehana. "Uncertainty Modeling For River Water Quality Control." Thesis, 2008. http://hdl.handle.net/2005/848.
Повний текст джерелаАнотація:
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.
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Arnold, Roderick William. "Modelling water quality in the upper Klip river." Thesis, 2014. http://hdl.handle.net/10539/15561.
Повний текст джерела