Water Quality Management Model Research Articles

Calibration, validation and uncertainty analysis of a SWAT water quality model

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.

Assessment of Existing Fate and Transport Models for Predicting Antibiotic Degradation and Transport in the Aquatic Environment: A Review

In recent years, the use of antibiotics for human medicine, animal husbandry, agriculture, aquaculture, and product preservation has become a common practice. The use and application of antibiotics leave significant residues in different forms, with the aquatic environment becoming the critical sink for accumulating antibiotic residues. Numerous studies have been conducted to understand antibiotic removal and persistence in the aquatic environment. Nevertheless, there is still a huge knowledge gap on their complex interactions in the natural environment, their removal mechanism, and the monitoring of their fate in the environment. Water quality models are practical tools for simulating the fate and transport of pollutant mass in the aquatic environment. This paper reports an overview of the physical, chemical, and biological elimination mechanisms responsible for the degradation of antibiotics in natural surface water systems. It provides an in-depth review of commonly used quantitative fate models. An effort has been made to provide a compressive review of the modeling philosophy, mathematical nature, environmental applicability, parameter estimation, prediction efficiency, strength, and limitation of commonly used environmental antibiotic fate models. The study provides information linking paradigms of elimination kinetics and their simulation in the antibiotic fate models aiming at critical issues regarding current model development and future perspectives and to help users select appropriate models for practical water quality assessment and management.

Development of a Water Quality Management Model for Dry Rivers in Arid Regions: Application on Wadi Rumah, Saudi Arabia

In dry rivers, urban wastewater discharge forms a pond in the dry period, and surface runoff shapes a longer pond in the wet period. A water quality management model developed for dry rivers is applied on Wadi Rumah in Saudi Arabia. The sub-basin was delineated from 30-m × 30-m spatial resolution digital elevation model. Hydrologic loss, runoff rates, and time of concentration were determined using the soil conservation service curve number, soil conservation service unit hydrograph methods, and Kirpich formula. Hydrologic Engineering Center Hydrologic Modeling System estimated the wet weather flows for 10 low-to-medium rainfall events. A steady-state hydrodynamic model including river flows, effluent discharge, and evaporation and infiltration losses estimated the hydraulic retention time for the explicit case of ponding conditions of dry rivers. Mass balance and first-order kinetics modeled the physical (total dissolved solid and total suspended solid), chemical (biochemical oxygen demand, dissolved oxygen, chemical oxygen demand, NH3, PO4, and Cl2), and microbiological (total coliform and fecal coliform) parameters. The hydrodynamic model and biokinetic rate coefficients were calibrated with satellite images, reported literature, and field measurements. A water quality index compared the aggregated water quality against the standards for 4 intended uses. The study yields “very good” water quality for restricted and unrestricted irrigation for the dry period, “fair” for livestock drinking, and “marginal” for fishery and recreation for the dry period, while water quality remained “marginal” for all the uses in the wet period. Water quality management implies regular water quality monitoring, continuous improvement of treatment facilities, and flood plain management initiatives for the long-term sustainability of dry rivers in arid regions.

Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour

Nonpoint source pollution from agriculture is one of the main causes of water quality degradation. To cope with this issue, diverse policy measures have been implemented to promote farming practices that favour water quality. Among these measures, many instruments are based on the voluntary commitments of farmers. Farmers'’ participation is therefore important for water quality outcomes. To analyse this issue, we built an agent-based model of a drinking water catchment area using the social–ecological systems framework and the theory of planned behaviour. This model aims to better understand the impact of behavioural dynamics on the effectiveness and efficiency of policies targeting water pollution. Our model allows us to explore how the evolution of farming practices is influenced by, first, the behavioural characteristics of farmers and, second, the characteristics of the policy measures implemented. Our main results are threefold: 1) The characteristics of farmers influence policy effectiveness and efficiency; 2) taking into account farmers' behavioural characteristics in the definition of policies may improve the efficiency of policies; and 3) in situations where behavioural characteristics are unknown, policies combining both financial incentives and training measures are costlier but may be a better option than policies relying on a single measure.

Integrated Water Quality Management Model for the Rural Transboundary River Basin—A Case Study of the Sutla/Sotla River

The intensive use of soil and water resources results in a disbalance between the environmental and economic objectives of the river basin. The water quality management model supports good water status, especially downstream of dams and reservoirs, as in the case of the Sutla/Sotla river basin. This research aims to develop a new, improved integrated water quality management model of rural transboundary basins to achieve environmental objectives and protection of the Natura 2000 sites. The model uses river basin pressure analysis to assess the effects of climate and hydrological extreme impacts, and a programme of basic and supplementary measures. The impact assessment of BASE MODEL, PAST, and FUTURE scenarios was modelled using the soil and water assessment tool (SWAT) based on land use, climate and hydrological data, climate change, presence or lack of a reservoir, and municipal wastewater and agriculture measures. Eight future climate change scenarios were obtained with optimistic (RCP4.5) and pessimistic (RCP8.5) forecasts for two periods (2020–2050 and 2070–2100), both with and without a reservoir. The model shows that the most significant impacts on the waterbody come from the nutrients and sediment hotspots, also shows the risk of not achieving good water status, and water eutrophication risk. The modelled average annual increase in sediment is from 4 to 25% and in total N from 1 to 8%, while the change in total P is from −5 to 6%. The conducted analysis provides a base for the selection of tailor-made measures from the catalogue of the supplementary measures that will be outlined in future research.

Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty

Although integrated simulation-optimization modeling can provide a comprehensive and reliable analysis for water quality management (WQM), it is usually not easy to implement in practice. This study proposed a new efficient simulation-optimization modeling approach by leveraging the power of data-driven modeling, to support WQM under various uncertainties. A water quality simulation model is integrated with the optimization model, and then substituted by a series of numerical surrogate models based on inexact linear regression. The transformation can significantly reduce the computational burden and make it possible to implement uncertainty quantification through hybrid inexact programming. The proposed model incorporates interval quadratic programming and credibility constrained programming to deal with nonlinearity and various uncertainties associated with the management system. The proposed approach is applied to a real case study of the Grand River watershed in Canada for controlling phosphorus concentration in river water. The Grand River Simulation Model (GRSM) is employed as the physical simulation model to estimate the total phosphorus concentration in the river. Interval solutions under different confidence levels of violating the effluent standards were obtained, which can be used to generate optimal phosphorus control strategies. The results indicate the proposed data-driven interval credibility constrained quadratic programming (DICCQP) model is able to provide reliable and robust solutions for WQM by considering nonlinearity and various uncertainties while maintaining a high computational efficiency. The proposed new framework can be extended and applied to the other watersheds. The high efficiency of the proposed model makes it possible to solve large-scale complex water quality management and planning problems.

Application of probabilistic bankruptcy method in river water quality management

In this paper, a probabilistic water quality management model is developed to present strategies using bankruptcy rules for solving conflicts between the Environmental Protection Agency and polluters in river systems. The bankruptcy concepts are adapted to the water quality aspect, Dissolved Oxygen as the water quality factor, and the pollutant concentration refers to the asset and stakeholders' claim. Bankruptcy rules are developed to allocate wastewater cooperatively and improve the water quality at the checkpoint. Therefore, a simulation–optimization model, including QUAL2Kw and Particle Swarm Optimization, is used to optimize the bankruptcy method’s waste load allocation. In the probabilistic model, the effect of river flow uncertainty on the optimal solution is investigated by Monte Carlo and Latin Hypercube Sampling. The optimal Dissolved Oxygen values are obtained corresponding to the possibility of river flows under the bankruptcy rules. The results of deterministic and probabilistic models show that the methodology reduces the waste load by 65–94% and increases Dissolved Oxygen from 0.9 to 5 mg/L. However, the streamflow uncertainty benefits polluters and allows them to release pollution more than twice the deterministic model. Analyzing the rules reveals that the Talmud rule outperformed others with higher Dissolved Oxygen and waste load criterion. This reliable probabilistic model can be used when the parties' performance is not cooperative, leading to more adaptability with real situations.

Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada

Effective river water quality management and planning is a complex issue challenged by various complexities and uncertainties. A simulation-based interval chance-constrained quadratic programming (ICCQP) model is developed for the seasonal planning of water quality management (WQM) under various uncertainties. The proposed model incorporates interval quadratic programming, chance-constrained programming, and a seasonal water quality simulation model within a general framework for WQM. Uncertainties associated with the objective and the coefficients in the left-hand sides of the constraints are tackled as intervals. Meanwhile, parameter uncertainties on the right-hand sides are characterized using probability distributions. Nonlinearities in the cost function are reflected by quadratic programming. A multi-segment water quality model is used to simulate the dynamic interactions between wastewater discharges and river water quality. The proposed ICCQP-WQM model is applied in a real case study for the control of total phosphorus (TP) in the central Grand River in Ontario, Canada. The results demonstrate that the proposed model is able to incorporate uncertainties expressed as intervals and probability information into an optimization framework and provide interval solutions. Thus, different cost-effective schemes for seasonal WQM could be generated. The results show the Kitchener wastewater treatment plant (WWTP) affects the value of the objective function more than the other WWTPs in the study area. It is also found that the Kitchener WWTP's cost accounts for the highest proportion (approximately 35.1–37.9%) of the total annual cost, which implies the control of TP at the Kitchener plant is the most important to the system. Moreover, river water TP standards in spring and autumn are usually difficult to meet, indicating different TP control strategies are needed in these two seasons. The generated results are valuable for local decision makers to generate TP control strategies, and also to identify optimized solutions under various uncertainties. The proposed ICCQP-WQM model can be extended to other watersheds to support effective water quality management and planning.

Need for Process Based Empirical Models for Water Quality Management: Salinity Management in the Delaware River Basin

AbstractManaging salinity in the Upper Delaware Estuary is an important operational goal within the Delaware River Basin (DRB). High salinity concentrations can create water quality and operational...

Applying a deployment strategy and data analysis model for water quality continuous monitoring and management

In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an Internet of things water quality monitoring network that integrates domestic miniaturized water quality monitoring sensors for real-time transport data of pH, temperature, conductivity, chemical oxygen demand, and copper ions. The data will be used to establish an analysis model based on continuous monitoring of the nation’s background concentration. We designed an automatic continuous monitoring and early warning analysis module for automatic analysis of environmental and instrumental anomalies for decision makers, a “pollution source analysis module” utilizing static and dynamic cross-environment data to swiftly trace upstream pollution sources, and a “pollution hotspot analysis module” to evaluate the impact area of pollutants, and immediate response measures to achieve early warning and swift evaluation for the prevention of water pollution. To do this, we installed 100 domestic miniaturized water monitoring devices in Taoyuan City for testing the solution. We found that the establishment of an Internet of things environment analysis and response model integrated with cross-environment analysis can be applied in water quality monitoring and management to assure improved environmental quality.

Development of an interval quadratic programming water quality management model and its solution algorithms

Interval quadratic programming (IQP) is one of the most popular programming techniques for addressing the uncertainties associated with nonlinear environmental management problems. However, efficient algorithms for solving IQP problems in water quality management (WQM) have not been well studied. In this study, an IQP model is developed for WQM under uncertainty. Three solution algorithms, including a piecewise linear approximation (PLA) method, a derivative algorithm (DEA) and a duality-based algorithm (DUA) are proposed for solving the IQP-WQM problem. The developed model and the corresponding solution algorithms are applied to a hypothetic WQM problem to demonstrate their applicability. The results show the lower bounds of the total cost obtained by three algorithms have a relationship of fDUA−=fDEA−≤fPLA−, while that of the upper bounds is fDEA+≤fPLA+≤fDUA+. Moreover, the sensitivity analysis shows that no matter how the IQP-WQM model is solved, the model response from the three solution algorithms is consistent. The results indicate that all of the three algorithms can efficiently deal with quadratic programming problems under uncertainties in the format of intervals. Comparison among the three algorithms shows that DUA provides interval solutions with wider ranges than the other two methods, and it requires less computational efforts than DEA. It is also found that PLA is more flexible and might require lower computational efforts for large scale problems. This study can provide useful technical support for effective WQM.

A simulation-based bi-level multi-objective programming model for watershed water quality management under interval and stochastic uncertainties.

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities.

Modeling of extreme risk in river water quality under climate change

Abstract A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2°C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020–2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

A water quality management model considering impacts of water utilization and providing risk analysis under uncertainty: a case study of the Ankang section in Hanjiang River Basin, China

A water quality management model considering impacts of water utilization and providing risk analysis under uncertainty: a case study of the Ankang section in Hanjiang River Basin, China

An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

ABSTRACTIn this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.

Withdrawn: Dissolved oxygen model for a river with extreme flow variations

This paper has been withdrawn by the authors due to errors in the results. A dissolved oxygen (DO) model is calibrated under low and medium flow conditions, while verification of the model is done under low flow condition, for a heavily polluted river. The Soan River is located in Pakistan, with large flow variations. The biokinetic rate coefficients for nitrogenous biochemical oxygen demand and carbonaceous biochemical oxygen demand (CBOD) are determined through ammonia river profiles and measured CBOD. A close agreement between the DO model results and the field values shows that the verified model can be used to develop DO management strategies for the Soan River. The biokinetic coefficient is known to vary with the degree of treatment (DOT) and needs to be adjusted for a rational water quality management model. The effect of this variation on the level of treatment has been evaluated by using the verified model to attain a DO standard of 4 mg/l in the river using the biokinetic rate coefficient. The required DOT in this case is found to be 92%, while the DOT is 82% if the adjusted biokinetic rate coefficient is used to reflect the effect of wastewater treatment. This concludes the use of appropriate biokinetic coefficients to manage water quality in rivers as highly important.

A Risk-Based Balance Inexact Optimization Model for Water Quality Management with Sustainable Wetland System Development—A Case Study of North China

In this study, a risk-based balance inexact water resources optimization model for considering wetland ecological water demand and water quality problems, based on interval-parameter programming, fuzzy two-stage stochastic programming, and downside risk-aversion measure, is developed for regional water resources management in Nansihu Lake basin, Shangdong province, China. The developed model can tackle uncertainties described in terms of interval values and probability distributions. Moreover, risk aversion is incorporated by limiting the volatility of the expected profit through downside risk methodology, in order to limit the risk of failing to reach an income target of competitive regions in the lake basin and reflect the preference of decision makers, such that the tradeoff between system economy, ecological water demand, and income target could be analyzed. All suggested scenarios (e.g. the plausibility degree of ecological water demand and the risk level of unbalance income) are determined by management requirement. The results indicated that different water inflow and ecological-related water demand levels correspond to different water shortages and allocation schemes of different water sources, and thus lead to varied system benefit and system-failure risk. The proposed model is valuable for supporting mid-/long-term water resources management under economic, environmental, ecological, and system balance development considerations.

An Inexact Two-Stage Water Quality Management Model for Supporting Sustainable Development in a Rural System

Water quality management is essential for preserving valuable water resources and facilitating sustainable socio-economic development in watershed systems. However, it is challenging for decision makers to identify desired schemes for management of economic development and environmental protection due to complexities of rural systems. In this study, an inexact two-stage water quality management (ITWQM) model is developed for supporting economic and environmental management of a rural area in Heshui Watershed. In the modeling formulation, pollutant discharge and soil loss are allowed under a range of relaxed constraints in association with a variety of probabilities, such that the reliability of satisfying (or risk of violating) system constraints can be analyzed; besides, penalties are imposed when policies expressed as allowable pollutant discharge levels are not satisfied. Solutions in connection with regional sustainable development such as land use, water quality protection, crop cultivation, livestock husbandry, forest expansion, fishery and industrial production, have been obtained. The results can be used for generating decision alternatives and helping local managers to identify desired policies under various environmental, economic, and system-reliability conditions. Decisions at a lower risk level would lead to an increased reliability in fulfilling environmental requirements but with a reduced system benefit; conversely, a desire for increasing system benefit could result in a raised risk of violating environmental constraints.

An approach to mathematical models as a tool for water and air quality management

Interactions between mathematical and biological sciences have been increasing rapidly in recent years. The use of system analysis and mathematical model for formulation and solving the environmental pollution is of relatively recent vintage and has been used widely since last three decades. These models can be used to conduct numerical experiments, test hypothesis and help to understand the response of environmental pollution. A mathematical model acts as a bridge between study of mathematics and application of mathematics in environmentand other fields. Modeling is an abstraction of reality and its ultimate objective is to explore the complexity of functions and structure of the system under study. Today, a wide variety of models belonging to different nature and category are available to understand the processes of the environment around us. Various models such as WASP, CE-QUAL-ICM, QUAL W2, AQUATOX, QUAL2K, IITAQ, PEARL, GRAM, UGEM, and IITLT etc. related to water and air quality are developed so far along with their principles, intended use and applications. These models generally simulate the basic physical, chemical and biological processes. In the present study, an attempt has been made to evaluate the concept and utilization of mathematical models in air and water quality management.

Comparisons of statistical models to predict fecal indicator bacteria concentrations enumerated by qPCR- and culture-based methods

Recently, the United States Environmental Protection Agency (USEPA) revised their recreational water quality criteria, in which adjustments were made by approving enterococci (ENT) quantitative PCR (qPCR) as an alternative, rapid method and advocating the use of predictive models for water quality management. The implementation of qPCR-based methods and prediction models are meant to decrease the time between sample collection and public advisories and notifications. To date, few studies have compared qPCR-based models to culture-based prediction models and none of these studies have been conducted in coastal estuarine systems. In this study, we created prediction models using qPCR-based fecal indicator bacteria (FIB) data in dual-use recreational and shellfish harvesting waters and compared them to published ENT and Escherichia coli (EC) culture-based prediction models in eastern North Carolina estuaries. Furthermore, an empirical statistical model was created to predict qPCR inhibition levels so that proper remediation techniques can be applied when it is a problem. Predictor variable selection in both qPCR- and culture-based ENT models was very similar; both models included 14-day rain total, dissolved oxygen, and salinity/conductivity, with 89 and 90% of qPCR and culture data described, respectively. Using ENT management action thresholds, qPCR- and culture-based models showed high accuracy in management decisions. The qPCR model had 92 and 96% accuracy using the 110 and 1000 cell equivalents (CE)/100 ml thresholds, respectively, and the culture model had 90% accuracy in management decisions with the 110 MPN/100 ml threshold. EC models for qPCR- and culture-based concentrations used similar independent variables (14-day humidity, salinity/conductivity, a rain/storm variable, and a measure of air temperature), with each model explaining 26 and 55% of the data variation, respectively. When using different thresholds that were logs apart for management decisions, the two EC models accurately predicted management decisions; qPCR models correctly predicted management decisions 96 and 77% of the time (using 31 and 320 CE/100 ml, respectively) while culture models correctly predicted management decisions 96 and 88% percent of the time (with 31 and 320 MPN/100 ml, respectively). Equivalency between models was shown in our non-point source impacted estuaries, with ENT models performing slightly better than EC models. In addition, inhibition of the qPCR was a major issue that had to be addressed. An inhibition model was created with easily obtained meteorological data and accounted for a high level of data variability (adjusted R2 = 0.82).