River Quality Management Research Articles

Uncertainty analysis in river quality management considering failure probability: controllable and uncontrollable input pollutants.

River quality management involves complex challenges due to inherent uncertainties in various parameters, especially when dealing with controllable and uncontrollable pollutants. This study integrates a finite volume approach, called SEF (symmetric exponential function), with Monte Carlo simulations in MATLAB to solve the advection-dispersion equation, focusing on evaluating river quality protection tools by considering failure probability (Pf). Critical specifications for maintaining reliable river ecosystem performance are identified. We simulate assimilation capacity for managing river water quality against controllable pollutants to satisfy allowable pollution concentration at the high-reliability index. Using the Genetic Programming (GP) algorithm, a new accurate equation for assimilation capacity calculation is presented considering Pffor the first time. Results indicate that flow velocity significantly affects river assimilation capacity: increasing velocity can shift the river to a hazardous state while decreasing it allows for greater pollutant assimilation. Sustainable protection tools, including dilution flow and detention time, are considered to manage uncontrollable pollutants within a specific time (Tc) and river length constraints (Lc), safeguarding river water quality for both human and animal populations. Dilution flow is practical for specific base velocities but ineffective at high base flow rates. Conversely, detention time consistently protects water quality across all base flow velocities within the Lc constraint. Moreover, this study introduces the ratio of detention time to initial pollution contact duration as a vital water quality index to protect the rivers' environment. Combining numerical methods with reliability analysis and soft computing techniques, this research provides valuable insights into river system dynamics and protecting river water quality.

River quality management: Integrating uncertainty, failure probability, and assimilation capacity

Managing river water quality is challenging due to uncertainties in hydraulic and hydrologic parameters. This study integrates the symmetric exponential function (SEF) approach for solving the advection-dispersion equation with the Monte Carlo method in MATLAB. This combination allows us to explore the river's assimilation capacity and the failure probability (Pf) of maintaining desired water quality standards. Here, Pf represents the likelihood of pollutant concentrations exceeding acceptable limits under varying river conditions. A key contribution of this study is the introduction of a novel equation, developed using the Genetic Programming (GP) soft computing tool, to calculate assimilation capacity considering the failure probability of water quality provision. This equation provides a valuable tool for risk assessment in water resource management by quantifying pollutant assimilation dynamics. Its robustness is validated through high Coefficient of Determination (R2) and Overall Index (OI) values near 1, along with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study identifies critical river characteristics, such as flow velocity and pollutant load, significantly influencing the reliability index (β). By outlining how adjustments in these parameters can achieve a target reliability index (β=4.526), our study offers a practical approach to safeguarding river ecosystems. For example, increasing flow velocity by 76 % can shift the river from a safe state (Pf=3×10−5) to a hazardous state (Pf=1), while a 44 % decrease in velocity allows for 57 % more pollutant assimilation. These findings highlight the importance of flow control as a cost-effective strategy for mitigating high pollutant concentrations and ensuring sustainable water quality management. By integrating numerical approaches with reliability sampling methods and soft computing techniques, this study enhances understanding of river system dynamics and supports informed decision-making for protecting water resources.

A timetable and spatial planning for pollutant entrance to the river

Pollution of water resources particularly surface water and rivers may affect human health and the environment seriously. So it is essential to find an efficient solution to control the pollution at rivers in order to reduce damages for consumers and protect the environment. At this research, a mathematical model is proposed to manage pollutant entrance to the river from several resources. The objective is to minimize the rate of pollution damage for consumers by using an analytical solution method and the particle swarm optimization algorithm as river quality management. Two different scenarios for consumption are considered in Gheshlagh River which is selected as a case study. A total of 192 decision variables corresponding to the mass of entering contamination at different hours of the day and 8 decision variables corresponding to the location of entering are considered. In the first scenario, at some hours of the day downstream consumption is zero, and in the second scenario, for all hours of the day, water is consumed at the downstream of Gheshlagh River. The achieved results indicate that the value of objective function for the first scenario, after optimization, decreased from 3513 to 48 kg in its optimal condition. In river quality management planning of two scenarios, the concentration of pollution do not exceed from allowable limit. According to the results, a timetable and spatial planning for pollutant entrance to the river can decrease the rate of pollution damage for consumers.

Total Maximum Daily Load of the Curug Sub-district Segment of The Cirarab River, Indonesia

Although The Cirarab River has a highly polluted status, it is used as a source for clean and drinking water for people in Tangerang City. Pollution occurs due to poor river quality management. This study examines the Total Maximum Daily Load/TMDL of The Cirarab River at The Curug Subdistrict Segment using QUAL2Kw with two modelling scenarios. The first scenario was used to obtain the existing load, and the second scenario was used to determine TMDL. The existing loads results obtained were 12,032.26 kgBOD/day and 48,946.69 kgCOD/day. The TMDL results obtained were 2,234.30 kgBOD/day and 18,619.20 kgCOD/day. The existing loads entering each subsegment have exceeded TMDL that they need to be reduced. TMDL calculation is useful as a basis for an effective river management strategy.

Reasserting the primacy of human needs to reclaim the ‘lost half’ of sustainable development

The concept of sustainable development evolved from growing awareness of the interdependence of social and economic progress with the limits of the supporting natural environment, becoming progressively integrated into global agreements and transposition into local regulatory and implementation frameworks. We argue that transposition of the concept into regulation and supporting tools reduced the focus to minimal environmental and social standards, perceived as imposing constraints rather than opportunities for innovation to meet human needs. The aspirational ‘half’ of the concept of sustainable development specifically addressing human needs was thus lost in transposing high ideals into regulatory instruments. The Sustainable Development Goals (SDGs) restore focus on interlinked human needs, stimulating innovation of products and processes to satisfy them. Through three case studies – PVC water pipes, river quality management in England, and UK local air quality management – we explore the current operationalisation of the concept in diverse settings, using the SDG framework to highlight the broader societal purposes central to sustainable development. Partnerships involving civil society support evolution of regulatory instruments and their implementation, optimising social and ecological benefits thereby serving more human needs. Restoring the visionary ‘lost half’ of sustainable development – meeting human needs in sustainable ways – creates incentives for innovation and partnership; an innovation framework rather than a perceived constraint.

Development and testing of a fast conceptual river water quality model

Modern, model based river quality management strongly relies on river water quality models to simulate the temporal and spatial evolution of pollutant concentrations in the water body. Such models are typically constructed by extending detailed hydrodynamic models with a component describing the advection-diffusion and water quality transformation processes in a detailed, physically based way. This approach is too computational time demanding, especially when simulating long time periods that are needed for statistical analysis of the results or when model sensitivity analysis, calibration and validation require a large number of model runs. To overcome this problem, a structure identification method to set up a conceptual river water quality model has been developed. Instead of calculating the water quality concentrations at each water level and discharge node, the river branch is divided into conceptual reservoirs based on user information such as location of interest and boundary inputs. These reservoirs are modelled as Plug Flow Reactor (PFR) and Continuously Stirred Tank Reactor (CSTR) to describe advection and diffusion processes. The same water quality transformation processes as in the detailed models are considered but with adjusted residence times based on the hydrodynamic simulation results and calibrated to the detailed water quality simulation results. The developed approach allows for a much faster calculation time (factor 105) without significant loss of accuracy, making it feasible to perform time demanding scenario runs.

Integrated river quality management by CCME WQI as an effective tool to characterize surface water source pollution (Case study: Karun River, Iran)

Evaluation of surface water quality is a complex process undertakingmultiple parameters. Converting great amount of parameters into a simpler expressionand enabling easy interpretation of data are the main purposes of water quality indices.The main aim of this study is to plan effective water resources management system forKarun River by combination of CCMEWQI and Geographic Information System (GIS).The investigation was carried out to set a management plan through exploratory andspatial analysis of physicochemical water parameters of collected samples from 10stations over one year period. Since all indices were obtained from index, river zoningwas conducted by GIS. Moreover, trace metals concentrations (As, Cr, Cd, Fe, Zn, Mn,and Al) ranged in safer limit. The highest values of F1 belonged to aquatic life and thelowest ones belonged to irrigation. Aquatic life and drinking uses received the maximumvalues of F2. The lowest values were devoted to livestock and then recreation uses. It wasinferred from index that the quality of the Karun River is principally impacted by highturbidity, TDS, NO3, SO4, and PO4 due to high suspended sediment loads. The maincause is incremental agricultural, industrial, and residential effluents. Amongst stations,station one only received the priority for drinking water supply and recreation.

Prediction and assessment of drought effects on surface water quality using artificial neural networks: case study of Zayandehrud River, Iran.

Although drought impacts on water quantity are widely recognized, the impacts on water quality are less known. The Zayandehrud River basin in the west-central part of Iran plateau witnessed an increased contamination during the recent droughts and low flows. The river has been receiving wastewater and effluents from the villages, a number of small and large industries, and irrigation drainage systems along its course. What makes the situation even worse is the drought period the river basin has been going through over the last decade. Therefore, a river quality management model is required to include the adverse effects of industrial development in the region and the destructive effects of droughts which affect the river’s water quality and its surrounding environment. Developing such a model naturally presupposes investigations into pollution effects in terms of both quality and quantity to be used in such management tools as mathematical models to predict the water quality of the river and to prevent pollution escalation in the environment.The present study aims to investigate electrical conductivity of the Zayandehrud River as a water quality parameter and to evaluate the effect of this parameter under drought conditions. For this purpose, artificial neural networks are used as a modeling tool to derive the relationship between electrical conductivity and the hydrological parameters of the Zayandehrud River. The models used in this research include multi-layer perceptron and radial basis function. Finally, these two models are compared in terms of their performance using the time series of electrical conductivity at eight monitoring-hydrometric stations during drought periods between the years 1997–2012.Results show that artificial neural networks can be used for modeling the relationship between electrical conductivity and hydrological parameters under drought conditions. It is further shown that radial basis function works better for the upstream stretches of the river while multi-layer perceptron is more efficient for the downstream stretches.

Development of River Quality Management (RQM) Information System for River Stretches Blending with Multi-Industrial Effluents

RQM information system takes into account various non-conservative pollution parameters like Total Dissolved solids (TDS), Total Suspended Solids (TSS), Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) and obtains the concentration of such parameters along the river stretch. The system incorporates various mathematical models for computation of velocity variation along the river stretch, flow rate computation, and cross- sectional area computation and finally uses a water quality dispersion model to compute the concentration of vivid pollutants at various points in the river stretch to predict the quality of river. Keywords—Information System, River Water Quality, Flow rate computation, Simulation of stream velocity, Multi-industrial outfalls, Water quality model

How to make river assessments comparable: A demonstration for hydromorphology

River monitoring and assessment programs are important tools to quantify the condition of river ecosystems, identify deficits, and provide preliminary indication of how to improve them. But, they are limited in delivering comparable assessment results across national or transnational borders, aggregating site-specific assessments into broader scale assessments, and supporting river management decisions. We present a multi-criteria decision analysis approach for improving the comparability of ecological assessment methods of different origin and for combining these assessments into a joint procedure. The approach consists of seven consecutive steps. The most central ones concern the hierarchical allocation of ecological assessment endpoints, and the harmonization of the scoring procedure of attributes (ecological indicators or assets) to a common scale from 0 to 1. We demonstrate the approach integrating three programs developed to assess the hydromorphological river condition in Switzerland, Germany, and the USA. In our example, the integrated assessment produces comparable results for the whole range from natural to impacted rivers, while data continuity with original assessments was maintained. Our approach provides a common assessment standard due to the definition of the minimum amount of information required, is flexible regarding measurement and assessment endpoints, and bridges the gap between river quality assessment and management.

Inexact Chance-Constrained Waste-Load Allocation Model for Water Quality Management of Xiangxihe River

AbstractIn this study, an inexact chance-constrained waste-load allocation (ICWA) model is proposed for supporting river quality management under uncertainty that is capable of addressing uncertainties expressed as intervals and probability distributions as well as analyzing the reliability of satisfying (or the risk of violating) various constraints. The ICWA model is then applied to water quality management of the Xiangxihe River in the Three Gorges Reservoir Region, which faces severe water quality problems due to point and nonpoint source pollution. The results demonstrate that higher biological oxygen demand (BOD) discharge amounts are observed at the Baishahe chemical plant and Gufu wastewater treatment plant. Moreover, total phosphorus discharges into the river come mainly from point sources, particularly the chemical plants and phosphorus mining companies. For nonpoint sources, crop farming generates large amounts of total phosphorus and total nitrogen. Tradeoffs between economic benefits and syst...

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.

A System Dynamics Model for Water Quality Management - Waste Load Allocation

A simulation-based system dynamics (SD) model was developed for allocating the waste load and supporting river quality management. Panjin section of Liaohe River in China was used as a study case to demonstrate the feasibility of the proposed methodology. The national surface-water environment function zoning of Liaoning Province was presented as a constraint in order to calculate the maximum allowable waste quantity for COD, which is expressed as interval format of both upper bound and lower bound. Then allocate the waste load to each point source responses within these quantities. The study results simulated from SD model, together with the intervals-format uncertainties of allowable COD level is valuable for supporting decision makers in optimizing water quality management.

Application of Automated Measurement Stations for Continuous Water Quality Monitoring of the Dender River in Flanders, Belgium

During the summer of 1999, two automated water quality measurement stations were installed along the Dender river in Belgium. The variables dissolved oxygen, temperature, conductivity, pH, rain-intensity, flow and solar radiation were measured continuously. In this paper these on-line measurement series are presented and interpreted using also additional measurements and ecological expert-knowledge. The purpose was to demonstrate the variability in time and space of the aquatic processes and the consequences of conducting and interpreting discrete measurements for river quality assessment and management. The large fluctuations of the data illustrated the importance of continuous measurements for the complete description and modelling of the biological processes in the river.

A Programming Model for River Quality Management under Transverse Mixing

The objective of river quality management is to achieve a given water quality standard at certain control stations by cutting down the pollutant load in the basin at the lowest cost. Conventional programming models, however, cannot guarantee the satisfaction of the quality standard at each drinking water intake when transverse mixing occurs in the river, because they focus on the average concentration of the cross sections, while the raw water is usually drawn from the sides of the river. In this paper, a programming model is proposed to deal with waste load allocation problems. The effect of transverse dispersion on the quality at drinking water intakes is taken into account by introducing an index which represents the degree of mixing in a river. This has made it possible to link the sewage system planning with the improvement of drinking water quality. The results of model application show that the priority of sewage treatment in a river basin is quite different from those advanced by the conventional models, it is suggested that a change of the traditional strategy of waste load allocation is necessary for better quality of drinking water supply.

Continuous effluent consents: Modelling and compliance testing

The control of continuous polluting discharges in the U.K. was placed in a framework of statistically based legislation nearly 10 years ago. Both discharge standards and desired river quality class objectives are assessed within a probabilistic system of pollution control whereby a minimum level of 95 percent compliance with standards has to be achieved. The use of such a statistical framework permits occasional infringements of what would otherwise be fixed standards. This enables the Water Industry to manage river quality without undue risk of prosecution or unnecessary capital expenditure on effluent treatment.The change to a statistically based system has been a slow process carried out in stages: from the introduction of a river quality classification; setting long term river quality objectives; then setting discharge consents to achieve these objectives; and finally monitoring compliance with the consents and objectives. Each of these stages has required the development of the necessary statistical tools for river quality planning and management. Due to the decentralised nature of the currently catchment-based Water Authorities, several different statistical approaches have been adopted. However, if, as is planned for the near future, river quality management is carried out by a national regulatory body, then some rationalisation of current methodologies will have to be undertaken.This paper introduces and examines current U.K. approaches to river quality management and pollution control. Particular emphasis is placed on the statistical modelling techniques used for consent setting and compliance testing. Some of the commonly used techniques are compared and evaluated. A description is presented of work that is underway to develop a framework for the establishment and assessment of intermittent pollution control criteria.

CHANCE-CONSTRAINED PROGRAMMING APPROACH TO MANAGING WATER QUALITY IN A RIVER WITH TRANSVERSE DISPERSION

A chance-constrained model is proposed for finding the optimal wastewater treatment level to control the water quality in a river which consists of several upstream tributaries and water intakes throughout its length and where transverse dispersion occurs. The model is applied to Yodo River basin. The results show that the order of priority of enhancing sewerage construction in three sub-basins changes when a stricter water quality standard is imposed. Means for river quality management are proposed based on the analysis of characteristics of transverse dispersion in the river and the distribution of discharges corresponding to water quality standard violations at water intakes.

Erratum: “River Quality Management under Stochastic Streamflow” by O. Fujiwara, S. Ohgaki and K. Gnanendran (April, 1986, Vol. 112, No. 2)

Erratum: “River Quality Management under Stochastic Streamflow” by O. Fujiwara, S. Ohgaki and K. Gnanendran (April, 1986, Vol. 112, No. 2)

River Quality Management under Stochastic Streamflow

A chance‐constrained model is proposed for the problem of finding the economically optimal treatment efficiency at each of several waste dischargers along a river basin while maintaining risks of stream quality violation in the different reaches below prescribed bounds. Streamflow is recognized as a random variable with a known probability distribution. This model overcomes a major drawback in a previous model, namely the requirement of a priori knowledge of the initial DO deficit at the top of every reach. The model is applied to the Hsintien River, Taiwan.

Water quality management with time varying river flow and discharger control

An extension of the classical river quality management problem is presented that allows for time varying operation of the treatment plants. The hydrology of the river is represented by a set of steady state flow regimes; a river water quality model is used for each of these regimes to give a constraint set to be imposed on the discharges in order to achieve the stream standards during that regime. The objective function distinguishes between investment costs, fixed operating costs, and variable operating costs. A treatment system is sought that minimizes the sum of these costs, taking account of the possibility of operating the treatment system at various different levels during the year. The model is quantified and tested on an example.