River Water Quality Management Research Articles

Mathematical Modeling for Water Quality Management under Interval and Fuzzy Uncertainties

In this study, an interval fuzzy credibility-constrained programming (IFCP) method is developed for river water quality management. IFCP is derived from incorporating techniques of fuzzy credibility-constrained programming (FCP) and interval-parameter programming (IPP) within a general optimization framework. IFCP is capable of tackling uncertainties presented as interval numbers and possibility distributions as well as analyzing the reliability of satisfying (or the risk of violating) system’s constraints. A real-world case for water quality management planning of the Xiangxi River in the Three Gorges Reservoir Region (which faces severe water quality problems due to pollution from point and nonpoint sources) is then conducted for demonstrating the applicability of the developed method. The results demonstrate that high biological oxygen demand (BOD) discharge is observed at the Baishahe chemical plant and Gufu wastewater treatment plant. For nonpoint sources, crop farming generates large amounts of total phosphorus (TP) and total nitrogen (TN). The results are helpful for managers in not only making decisions of effluent discharges from point and nonpoint sources but also gaining insight into the tradeoff between system benefit and environmental requirement.

PROPOSE AN APPROPRIATE MANAGEMENT MODEL FOR SAI GON RIVER WATER QUALITY

The article proposed an appropriate organization modeling for Sai Gon river water quality management based on the analysis having scientific and practical basic about aspects have done and aspects limited of LVS management organization (LVS environmental protection Committee) in past time, lesson learnt from effective LVS management performance of countries in the world as well as based on actual study changes in Sai Gon river water quality in many years and practically coordination management and environmental protection river among local area along river basin. The proposed modeling is feasible and practical aim to protect Sai Gon river water source serving for different purposes such as supply water for domestic demand, industry, irrigation, river landscape – tourism, and waterway etc., towards sustainable development of local area along river basin.

Cost Allocation of River Water Quality Management Considering Development in Upper Basin and Total Pollution Load Management System

Cost Allocation of River Water Quality Management Considering Development in Upper Basin and Total Pollution Load Management System

A Simulation-Based Optimization Approach for Water Quality Management of Xiangxihe River Under Uncertainty

A simulation-based interval quadratic programming (IQP) method was developed for river water quality management, where uncertainties associated with water quality parameters, cost functions, and environmental requirements were described as interval values. IQP was applied to a real-case study of the Xiangxihe River, China. A multisegment simulation model of one river with one tributary was used to generate water-quality transformation matrices and vectors, which were used to establish the river water quality constraints; in addition, the cost function of wastewater treatment were expressed as quadratic form. System cost, wastewater treatment efficiencies, and river pollution situations were analyzed under two scenarios and three criteria. A number of cost-effective schemes were generated by adjusting different combinations of decision variables within their solution intervals. Results can help decision makers generate alternatives between wastewater treatment cost and stream water quality requirement under uncertainty.

River water quality management under incomplete information: application of an N-person iterated signaling game

One of the important issues in river quality management is to provide pollution control strategies which are acceptable for all stakeholders. When there is only one water quality checkpoint in a reach of a river which receives pollution loads of several dischargers and dischargers are penalized for any water quality violation, the game theory can be used for modeling the natural process of bargaining among load dischargers considering the assimilative capacity of a river. There are also some types of uncertainties in river water quality management which should be incorporated throughout the bargaining process. Signaling games can be utilized for modeling the bargaining among dischargers and developing perfect Bayesian equilibrium (PBE) strategies for pollution control. In this paper, a new methodology called N-person iterated signaling game is developed for river quality management considering the existing uncertainties in pollution loads of dischargers. The methodology can provide the stable PBE waste load allocation strategies. The practical utility of the proposed methodology is illustrated by applying it to a reach of the Zarjub River in Iran. This reach includes seven pollution load dischargers.

Monitoring sediment oxygen demand for assessment of dissolved oxygen distribution in river

Sediment oxygen demand (SOD) has become an integral part of modeling dissolved oxygen (DO) within surface water bodies. Because very few data on SOD are available, it is common for modeler to take SOD values from literature for use within DO models. SOD is such an important parameter in modeling DO that this approach may lead to erroneous results. This paper reported on developing an approach for monitoring sediment oxygen demand conducted with undisturbed sediment core samples, where the measured results were incorporated into a water quality model for simulating and assessing dissolved oxygen distribution in the Xindian River of northern Taiwan. The measured results indicate that a higher freshwater discharge results in a lower SOD. Throughout a 1-year observation in 2004, the measured SOD ranged from 0.367 to 1.246 g/m(2)/day at the temperature of 20°C. The mean values of the measured SOD at each station were adopted in a vertical two-dimensional water quality model to simulate the DO distribution along the Xindian River. The simulating results accurately depict the field-measured DO distribution during the low and high flow conditions. Model sensitivity analyses were also conducted with increasing and decreasing SOD values for the low and high flow conditions and revealed that SOD had a significant impact on the DO distribution along the Xindian River. The present work combined with field measurements and numerical simulation should assist in river water quality management.

A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network

AbstractThe main objective of the present investigation is to predict longitudinal dispersion coefficient (Kx) in natural streams using artificial neural network (ANN) technique based on most famous training functions such as Trainlm, Trainrp, Trainscg, Trainoss, and so on. To achieve the goal, hydraulic and geometric data (shear velocity, channel width, local flow depth, and mean longitudinal velocity) that are easily obtained in natural streams are used. First, we have tried to review the most well‐known of published work in the field due to find out deficiencies of them. Second, new approach of ANN model based on the famous training functions is applied for predicting Kx in natural streams and then the best architectures for each training functions is selected by trial and error. Finally, Levenberg‐Marquardt training function (Trainlm) is selected as the best choice for training the network parameters. Determination coefficient (R2) and mean absolute error for ANN (Trainrp) model were equal to 0.94 and 33 in the training and 0.95 and 30 in the testing steps, respectively. It is hoped that the presented methodology in the research, can be useful in river water quality management studies. © 2010 American Institute of Chemical Engineers Environ Prog, 2011.

Spatial and Temporal Analysis of Nitrogen Transport and Transformation in Surface Water

Water quality in terms of nitrogen transport and transformations in surface water has been presented in both spatial and temporal distributions. The paper is concentrated on the analysis for the Lake Nasser, the main Nile with the two branches, the drains, irrigation canals and rayahs. The spatial variation of the sampled water quality parameters is presented with the comparison to the recommended standard (Law 48/1982). Further temporal analysis considering the previous campaign (September 2000) results is presented. The study concluded that the nitrate problem has been found in the drains where agricultural land is the main source of nitrate in the surface water in Egypt. The research also concluded that the problems of understanding the different relations between the water quality evolution, estimating the effect of river flow and water quality management projects, etc. be solved by analysis of monitoring results and simulation models can have a significant and decisive role. Furthermore, the study indicated that the water quality models are the tools for analyzing, extrapolating and predicting water quality. To reduce groundwater and surface water pollution and control the environmental cost to remove nitrate-nitrogen from water, it is essential to understand fully the nitrate leaching from agricultural fields. Finally, it is recommended to build up a conceptual simplified model for the point-sources locations to be able to propose different scenarios for improving the current state of the river and drains quality conditions.

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995–2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.

Comprehensive River Water Quality Management by Simulation and Optimization Models

In this paper, the performance of water quality modeling is investigated in the analysis of the natural assimilative capacity (NAC) of a river system. The new data available, along with the appropriate integrated model, allow for the analysis of the NAC of the entire river. The integrated model developed here emphasized the implementation of the QUAL2E simulation model and the appropriate allocation of waste loads (optimization model) in a subtropical area. Simulations are calibrated and validated according to the data from the Water Quality and Flow Rate Monitoring Project using the QUAL2E model. On the basis of the optimal allocation model, the results of the NAC and allowable pollution loading in the Putzu River basin are discussed. The study provides a framework of the integrated model that can be used as a guideline to determine the NAC of subtropical rivers.

A hydrodynamic water quality model for propagation of pollutants in rivers

Numerical modelling can be a useful tool to assess a receiving water body's quality state. Indeed, the use of mathematical models in river water quality management has become a common practice to show the cause-effect relationship between emissions and water body quality and to design as well as assess the effectiveness of mitigation measures. In the present study, a hydrodynamic river water quality model is presented. The model consists of a quantity and a quality sub-model. The quantity sub-model is based on the Saint Venant equations. The solution of the Saint Venant equations is obtained by means of an explicit scheme based on space-time conservation. The method considers the unification of space and time and the enforcement of flux conservation in both space and time. On the other hand, the quality sub-model is based on the advection dispersion equation. Particularly, the principle of upstream weighting applied to finite difference methods is employed. This method enable us to reduce the numerical dispersion avoiding oscillation phenomena. The optimal weighting coefficient was calculated on the basis of the mesh Peclet number. Regarding the quality processes, the model takes into account the main physical/chemical processes; these are degradation of dissolved carbonaceous substances, ammonium oxidation, algal uptake and denitrification, dissolved oxygen balance, including depletion by degradation processes and supply by physical reaeration and photosynthetic production. To properly simulate the river water quality, four state variables were considered: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project for which quantity and quality data were gathered. A sensitivity analysis of the model output compared to the model input or parameters was carried out.

A parsimonious dynamic model for river water quality assessment

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH(4), and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management.

Water Quality Assessment of Porsuk River, Turkey

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

Controlling wasteloads from point and nonpoint sources to river system by GIS-aided Epsilon Robust Optimization model

A stochastic multiobjective programming model, an ɛ-RO (Epsilon Robust Optimization) model as a simulation–optimization model with an embedding approach, is developed for river water quality management under hydro-environmental uncertainty. The model is a hybrid of the ɛ-constraint method and the robust optimization framework, which depresses the high sensitivity of the model to the input data uncertainties by introducing a plausible set of scenarios. Possible pollutant sources (all kinds of point and nonpoint pollutant sources excepting forests) are treated as controllable. The finite element method is employed for approximations to COD (Chemical Oxygen Demand) and DO (Dissolved Oxygen) transport equations with convection and dispersion terms. Realizations for the in-stream COD–DO interactive events, thus described in discrete forms, are embedded as equality constraints in the model. Controlling wasteloads from a variety of sources is implemented by seeking noninferior solutions (management alternatives) that maximize total COD load to the stream while minimizing COD load deviations and in-stream water quality violations. Demonstrative operation of the model is made with its application to the Yasu River, Japan. It is shown that there is indeed an alternative management strategy to improve in-stream water quality as a whole while increasing the total allowable load to the stream. The ɛ-RO model developed could thus be a viable alternative to the conventional river water quality management models.

A game theoretic approach for trading discharge permits in rivers

In this paper, a new Cooperative Trading Discharge Permit (CTDP) methodology is designed for estimating equitable and efficient treatment cost allocation among dischargers in a river system considering their conflicting interests. The methodology consists of two main steps: (1) initial treatment cost allocation and (2) equitable treatment cost reallocation. In the first step, a Pareto front among objectives is developed using a powerful and recently developed multi-objective genetic algorithm known as Nondominated Sorting Genetic Algorithm-II (NSGA-II). The objectives of the optimization model are considered to be the average treatment level of dischargers and a fuzzy risk of violating the water quality standards. The fuzzy risk is evaluated using the Monte Carlo analysis. The best non-dominated solution on the Pareto front, which provides the initial cost allocation to dischargers, is selected using the Young Bargaining Theory (YBT). In the second step, some cooperative game theoretic approaches are utilized to investigate how the maximum saving cost of participating dischargers in a coalition can be fairly allocated to them. The final treatment cost allocation provides the optimal trading discharge permit policies. The practical utility of the proposed methodology for river water quality management is illustrated through a realistic case study of the Zarjub river in the northern part of Iran.

Fuzzy optimization model for integrated management of total nitrogen loads from distributed point and nonpoint sources in watershed

A fuzzy optimization model is developed to allocate allowable total nitrogen (T-N) loads to distributed nonpoint sources (NPSs) and point sources (PSs) in a watershed for river water quality management using the linear programing technique. The watershed is divided into uniform grid cells on which T-N loads issuing from NPSs such as paddy fields, upland crop fields and cities are controlled. A geographic information system integrated with the digital elevation model facilitates computation of route lengths of surface and subsurface flows from cells to a river running through the watershed. The T-N loads discharged from their sources are assumed to decay, subject to distance-related first-order kinetics. As management goals, maximizations of total allowable NPS loads, total allowable PS loads and total yield of rice are considered from environmental and economic viewpoints. A prime constraint is an effluent limitation standard for the aggregate amount of loads that arrive at the downstream end of the river. The fuzzy sets theory helps appropriately describe vague attitudes of decision-makers (i.e., stakeholders and management authorities) in terms of constraints and conflicting goals. An application of the fuzzy optimization model, developed as an improvement over our last nonfuzzy model, to a real watershed in Shiga prefecture, Japan, demonstrates that the fuzzy model embodies our last model, and is capable of creating management alternatives for T-N load allocation in a more practical and flexible manner.

Modeling of nitrobenzene in the river with ice process in high-latitude regions

To deal with the accidental release of pollutants into frozen rivers, those rivers in high-latitude regions should be treated differently. Pollutants could be stored up in the ice and released when the ice melts in spring, perhaps resulting in secondary pollution in the river. A water quality model of nitrobenzene has been developed in this paper to assess the influence on river water quality due to the freezing process. The model is made up of three modules: thermodynamic module, hydrodynamic module and water quality module. The thermodynamic module considers the complex heat exchange processes between the water body and the atmosphere, the water body and the river bed, the water body and the ice cover, and so on. The growth of the ice cover is simulated in a simplified form whilst taking consideration of heat balance. The hydrodynamic model uses the Saint-Venant equations in non-constant flow and the influence of the ice cover is measured. The degradation, diffusion, absorption and desorption, and the influence of the freezing process are incorporated in the water quality module and the concept of Continuously Stirred Tanked Reactors (CSTRs) model is applied in the model construction. The model has been applied to supporting the management of accidental pollution in the Songhua River. The model was calibrated and validated with monitored data. Regional sensitivity analysis based on a task-based Hornberger-Spear-Young (HSY) algorithm was carried out to examine the model structure and the result showed that the model could describe the system very well. Then the conditioned model was applied to predicting the concentration of nitrobenzene in the water when the ice melted in spring. The predictive result showed that the release of pollutants in the ice could lead to an increase in the concentration of pollutants in the water, but the increase would be very small because there was only a small amount of pollutants stored in the ice. A secondary pollution could therefore be avoided. Also, with necessary modifications, the model established in this study could be applied to the water quality management in rivers that freeze in winter.

An Interval-Parameter Waste-Load-Allocation Model for River Water Quality Management Under Uncertainty

A simulation-based interval quadratic waste load allocation (IQWLA) model was developed for supporting river water quality management. A multi-segment simulation model was developed to generate water-quality transformation matrices and vectors under steady-state river flow conditions. The established matrices and vectors were then used to establish the water-quality constraints that were included in a water quality management model. Uncertainties associated with water quality parameters, cost functions, and environmental guidelines were described as intervals. The cost functions of wastewater treatment units were expressed in quadratic forms. A water-quality planning problem in the Changsha section of Xiangjiang River in China was used as a study case to demonstrate applicability of the proposed method. The study results demonstrated that IQWLA model could effectively communicate the interval-format uncertainties into optimization process, and generate inexact solutions that contain a spectrum of potential wastewater treatment options. Decision alternatives can be generated by adjusting different combinations of the decision variables within their solution intervals. The results are valuable for supporting local decision makers in generating cost-effective water quality management strategies.

Water Framework Directive catchment planning: a case study apportioning loads and assessing environmental benefits of programme of measures

Complying with proposed Water Framework Directive (WFD) water quality standards for 'good ecological status' in England and Wales potentially requires a range of Programmes of Measures (PoMs) to control point and diffuse sources of pollution. There is an urgent need to define the benefits and costs of a range of potential PoMs. Water quality modelling can be used to understand where the greatest impact in a catchment can be achieved through 'end of pipe' and diffuse source reductions. This information can be used to guide cost-effective investment by private water companies and those with responsibilities for agricultural, industrial and urban diffuse inputs. In the UK, river water quality modelling with the Environment Agency SIMCAT model is regarded as the best current approach to support decision making for river water quality management and planning. The paper describes how a SIMCAT model has been used to conduct a trial WFD integrated catchment planning study for the River Ribble catchment in the North West of England. The model has been used to assess over 80 catchment planning scenarios. The results are being used support a national assessment of the cost-effectiveness of proposed PoMs.

A Proposed Decision Support System for River Water Quality Management in India

The growing concern for river water pollution in India is witnessing several initiatives from nodal agencies in terms of water pollution control. In a similar context, a need for a supportive platform is envisaged that takes care of management perspectives, feasible for Indian conditions. In this paper the most generic DSS has been proposed that caters to the requirement of regulatory authorities. In its prototype scale of application to improving water quality, at user defined water quality criterion, the system is capable of allocating waste loads to point sources, while liberating the user from writing the input files for simulation and management models. The system is designed for PC-Windows environment with minimal system requirements in terms of models viz., QUAL2E water quality model and LINDO optimizing package, interfaced using SQL Server 2000 Data base Management System. While the most expedient approach of treatment at source towards water quality management in India was addressed by the system, the example run of the DSS indicated a need of including other management options for comprehensive quality management.