Water Quality Simulation Model Research Articles

Integrating water quality model and aeration with IoT technology in water quality management: A conceptual framework

Eutrophication poses a significant threat to both human population growth and aquatic life. It gives rise to a range of issues, including algae blooms, loss of habitat, reduced self-purification capacity, and changes in the biodiversity system. In order to restore the ecosystem, it is imperative to implement water quality management measures to combat eutrophication. Common methods for mitigating eutrophication include the use of water quality models, aeration, and IoT technology. Water quality model simulations have been demonstrated to accurately predict future water quality. Aeration, on the other hand, increases oxygen concentration in water through dispersion. Furthermore, the utilization of IoT in water quality monitoring provides users with precise and real-time data. Despite research findings that suggest the effectiveness of water quality models, aeration, and IoT technologies in addressing eutrophication, their current integration is inadequate. Therefore, the aim of this paper is to develop a conceptual framework that incorporates water quality models, aeration, and IoT technology to regulate water quality, with a specific focus on preventing the eutrophication issue. The conceptual framework was created by studying existing research on frameworks for water treatment, water quality modelling, aerators, and IoT technologies. Several adjustments were made to tailor the general framework to the specific requirements of this study. The discussion emphasizes the advantages of conceptual framework development in managing water quality, which integrates water quality models, aerators, and IoT technologies. This framework is expected to serve as an effective tool for managing eutrophication in water, while also promoting sustainable measures to address water contamination.

Phosphorus distributions in alluvial soils of the Lower Mississippi River Basin: A case of dual legacies.

Legacies can become intertwined, none more so than the body of work of Dr. Andrew Sharpley examining agricultural nutrient delivery to waterbodies and the phosphorus (P) accumulation in agricultural soils, or "legacy P." Although Sharpley's work focused on the anthropogenic influence on soil P, our study suggests soils of the Lower Mississippi Alluvial Plain (MAP) represent a natural legacy with moderate levels of available P resulting from minimal anthropogenic input. In 2019, we collected surface (0-5cm) soil samples from four regionally dominant soil series in either cropland or forested land uses, spanning 76 locations within the MAP. Soil chemical and physical properties were measured utilizing a suite of extractions and texture analysis to correlate properties with soil P values. Total soil P did not vary between land uses. Mehlich-3 extractable P was slightly higher in cropland soils due to higher concentrations in Forestdale and Sharkey soils. Dundee, Forestdale, and Sharkey cropland soils showed significant associations between Mehlich-3-extractable iron (Fe) and P. Ratios of total carbon (TC) to total nitrogen (C:N) and TC to P (C:P) were consistent across all sampled soil series but differed between forest and cropland soils. These ratios are critical for establishing baseline soil nutrient values in simulation models and can be used to improve water quality model simulations that help guide P management in the MAP. As Sharpley routinely demonstrated, understanding sources of P is critical for developing an appropriate management strategy. This study provides critical knowledge on soil P dynamics in the MAP region.

Development of a multi-objective reservoir operation model for water quality-quantity management

This study aims to develop a multi-objective quantitative-qualitative reservoir operation model (MOQQROM) by a simulation-optimization approach. However, the main challenge of these models is their computational complexity. The simulation-optimization method used in this study consists of CE-QUAL-W2 as a hydrodynamic and water quality simulation model and a multi-objective firefly algorithm-k nearest neighbor (MOFA-KNN) as an optimization algorithm which is an efficient algorithm to overcome the computational burden in simulation-optimization approaches by decreasing simulation model calls. MOFA-KNN was expanded for this study, and its performance was evaluated in the MOQQROM. Three objectives were considered in this study, including (1) the sum of the squared mass of total dissolved solids (TDS), (2) the sum of the squared temperature difference between reservoir inflow and outflow as water quality objectives, and (3) the vulnerability index as a water quantity objective. Aidoghmoush reservoir was employed as a case study, and the model was investigated under three scenarios, including the normal, wet, and dry years. Results showed the expanded MOFA-KNN reduced the number of original simulation model calls compared to the total number of simulations in MOQQROM by more than 99%, indicating its efficacy in significantly reducing execution time. The three most desired operating policies for meeting each objective were selected for investigation. Results showed that the operation policy with the best value for the second objective could be chosen as a compromise policy to balance the two conflicting goals of improving quality and supplying the demand in normal and wet scenarios. In terms of contamination mass, this policy was, on average, 16% worse than the first policy and 40% better than the third policy in the normal scenario. In the wet scenario, it was, on average, 55% worse than the first policy and 16% better than the third policy. The outflow temperature of this policy was, on average, only 8.35% different from the inflow temperature in the normal scenario and 0.93% different in the wet scenario. The performance of the developed model is satisfactory.

Low-carbon scheduling of electricity consumption in wastewater treatment plant by using photovoltaic system

Sewage treatment as a high energy consumption industry, its electricity consumption accounts for 3 % of the total electricity consumption of society. That means significant greenhouse gas emissions. In the context of China's goal of “reaching carbon peak by 2030 and achieving carbon neutrality by 2060”, reducing the energy consumption of wastewater treatment systems has emerged as an important issue in recent years. In this paper, the GPS-X simulation software was employed to conduct a simulation study of a modified Anoxic-Aerobic-Oxic wastewater treatment plant (WWTP) in Wuhan, and the response surface methodology (RSM) was utilized to ascertain the interactive effects of DO, IRF, ERR, and SD on the effluent quality, thereby identifying the operational parameters that minimize energy consumption while maintaining satisfactory effluent quality. Additionally, the PVsyst software was employed to design the solar power generation system of the WWTP and analyze its power generation potential. On this basis, through the coupling of photovoltaic power, electricity load, time-of-use pricing, and the water quality simulation model, and taking the WWTP data in September as a case study, the electricity usage strategies under various illumination conditions were formulated. The aim is to maximize the use of photovoltaic power to reduce the cost and carbon emissions of the WWTP. The results show that the optimal combination of operational parameters, including an external reflux ratio of 0.3, the internal recycle flow of 50,000 m3/d, and the sludge discharge of 448 m3/d, resulted in a reduction in power of 208.5 kW, and after the combination optimization of operational parameters and electricity utilization, the operation cost of the WWTP in September was reduced by 40 % ∼ 60 %, and the carbon emission attributable to electricity was reduced by 30 % ∼ 50 %.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Water quantity–quality assessment in the transboundary river basin under climate change: a case study

Abstract This study combined hydrological and water quality simulation models with a water resources planning model to project future water supply conditions under the dam construction in the Harirud River, located at the Afghanistan, Turkmenistan, and the Iran border. The sustainability requirements and possible conflicts among riparian countries were assessed under climate change and future development in Afghanistan's upstream. The water quantity and quality of the Doosti Dam Basin on the Harirud River were investigated based on a contemporary time (1955–2015) to predict the future condition (2020–2099). The representative concentration pathway scenarios were applied based on five bias-corrected climate models. Results showed that most areas of the study area experienced an increase in temperature (1.5–3.8°C) and a decrease in precipitation (19–24%). The Doosti Dam inflow decreased by about 70% after the Salma Dam construction, and the reliability and sustainability of agricultural water supply in Iran and Turkmenistan will reduce to less than 3% under the representative concentration pathways 8.5 climate change scenario. The results show that the Doosti Dam is not a reliable source to supply the domestic water demand of Mashhad, the second most important city in Iran.

Mapping and assessment of river water quality under varying hydro-climatic and pollution scenarios by integrating QUAL2K, GEFC, and GIS

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example.

The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.

Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks

Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel.

Effect of the Proposed Outlets on the Hydrodynamic Behavior and Water Quality of the South-West Part of the Al Hammar Marsh

The current study aims to find a new plan to manage the water quality of the western part of the Hammar Marsh to reduce the salts that cause problems for the marshes and preserve their environmental life by isolating the southwestern part of the Hammar Marsh by closing the outlet under the railway embankment. The outlet is discharging saline water to the east-western part of Al Hammar Marsh. After isolating the southwestern part of the marsh, a new outlet is proposed. The impact of the flow hydrodynamics on improving the water quality was simulated using the SMS model. The hydrodynamics and water quality simulation models for the marsh are : a hydrodynamic model and average depth (SMS RMA2) and a two-dimensional water quality model (SMS RM4). The Civil3D was used to determine the area elevation curve for the marsh, while the shape file for the study area was prepared using the Arc-GIS model. The first model is used to simulate the flow conditions in the marsh (water depth and velocity vectors), while the second model is used to simulate the salinity of the water (Total Dissolved Solids (TDS)). For calibration and verification the models, water samples were taken from ten selected locations within the marsh. The measurements were conducted on 1st January and on 2nd February 2022. The simulation results were validated with the field measurement, and the discrepancy between the simulated and measured water depth was found to be 11%. Many scenarios are based on the proposed recommended outlet that considerably reduces TDS concentration in the Al Hammar marsh. Three scenarios were run on the proposed outlet to maintain a submerged area of 88 km2, and to compare the three scenarios, ten points were selected in different locations, where the average TDS ratio for the first scenario was 7528 mg/l, the second scenario was 6982 mg/l and for the third scenario was 8069 mg/l. Results showed that the proposed outlet would improve the hydrodynamics of the flow and reduce the TDS concentration by 10% in addition to controlling the contamination of east western part of the marsh

A Novel Wastewater Load Allocation Approach for River Basins Using Simulation-Optimization Models

In this study, a new wastewater load allocation approach using a linked simulation-optimization model is proposed to determine the receiving body-based discharge limits by considering the discharge standards used by the European Union Water Framework Directive. By using the proposed approach, wastewater loads of point sources can be determined in such a way that the parameters exceeding the water quality targets (WQT) in receiving water bodiesmeet the relevant WQT. The simulation part is used to determine pollutantconcentrations throughout the river system using the AQUATOX water quality simulation model. However, since AQUATOX is an independent simulation modeland its source code is not publicly available, it is not possible to execute it with the optimization model for the generated load combinations. Therefore, a concentration-response matrix (CRM) is developed as a surrogate water simulation model by using the outputsoftheAQUATOX model. After this process, the developed CRMis integrated into an optimization model where the heuristic differential evolution (DE) optimization approach is used. The performance of the proposed simulation-optimization approach is evaluated on a sub-watershed of the Kucuk Menderes River Basin by considering different wasteload allocation scenarios for the CBOD5 water quality parameter. The results showed that the proposed simulation-optimization approach can effectively allocate the wastewater loads among different point sources by considering the WQT values of the CBOD5 parameter.

Formulated Mathematical Model for Delayed Particle Flow in Cascaded Subsurface Water Reservoirs with Validation on River Flow

Migration of pollutant particles into subsurface water reservoirs through point sources is largely involved mixing processes within the system of water flow. Possible potential sources of pollution to these point sources include municipal wastes, septic loads, landfills, uncontrolled hazardous wastes, and sewage storage tanks. The mixing processes of pollutant significantly alter their predictive rate of flow in the water reservoirs, and therefore the time inherent in mixing processes need to be accounted for. In this study, pollution of subsurface water reservoirs mainly rivers and streams through contaminated water point sources (CWPS) was studied through a conceptual perspective of mixing problem processes in water tanks. The objective was to formulate a discrete time delay mathematical model which describes the dynamics of water reservoir pollution that involve single species contaminants such as nitrates, phosphorous, and detergents injecting from a point source. The concentration x t of pollutants was expressed as a function of the inflow and outflow rates using the principle for the conservation of mass. The major assumption made in modeling of mixing problems using tanks is that mixing is instantaneous. Practical realities dictate that mixing cannot occur instantaneously throughout the tank. So as to accommodate these realities, the study refined the systems of ordinary differential equations (ODEs) generated from principles of mixing problems in cascading tanks, into a system of delayed differential equations (DDEs) so that the concentration of pollutant leaving the reservoir at time t would be equal to the average concentration at some earlier instant, t − τ for the delay τ > 0 . The formulated model is a mathematical discrete time delay model which can be used to describe the dynamics of subsurface water reservoir pollution through a point source. The model was simulated on municipal River Nyakomisaro in Kisii County, Kenya. Physical and kinematic parameters of the river (cross-sectional lengths, depths, flow velocities) at three river sectional reservoirs were measured and the obtained parameter values were then used to evaluate coefficients of the formulated model equation. The system of DDEs from this simulation was solved numerically on MATLAB using dde23 software. From the graphical views generated for concentration of pollutant x t versus time t , it was established that the developed DDEs cover longer time series solutions (characteristic curves) than that from the corresponding ODEs in the same reservoir indicating that time necessary for particle flow through water reservoirs is underestimated if ODEs are used to describe particle flow. Also, the graphical views indicated similar tendencies (characteristics) in particle flow with time elapse even though initial values of concentration x t were different for every potentially recognized single species pollutant considered in each river reservoir. Hence, longer values of time t will imply more pollution in the water reservoir and vice versa. By introducing time delays due to constituent mixing processes in water quality simulation models that make use of advection-diffusion equation such as Qual2kw, the findings of this study can help for better understanding of the contaminant’s accumulation levels and their rate of transport in water resource. These will assist, for example, water-quality protection agencies such as Environmental Protection Agency (EPA), World Health Organization (WHO), and National Environmental Management Authority (NEMA) for the need to generate efficient and effective remedial strategies to control or mitigate hazardous or risks arising from water pollution.

Increased risk of water quality deterioration under climate change in Ganga River

The industrialized stretch of Kanpur is considered to be one of the most polluted stretches of the Ganga River, with untreated sewage, industrial discharge, and agricultural runoff. Risk assessment studies on water quality for future scenarios are limited for this stretch of the river. In this study, we assess the effect of climate change on water quality, the risk of eutrophication, and fish kill for the mid and end of the twenty-first century for this river stretch. The water quality parameters considered are dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonia, nitrate, total nitrogen (TN), organic-, inorganic- and total phosphorous (TP), and fecal coliform (FC). 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 to a water quality simulation model. Our simulations indicate a potential deterioration of water quality in this stretch in the mid-twenty-first century, with a potential increase in pollutant concentration by more than 50% due to climate change alone. However, a slight improvement is simulated by the end of the century relative to the mid-twenty-first century which can be attributed to increased streamflow during low-flow periods due to increased summer mean precipitation. The risk of reduced dissolved oxygen and increased organic and nutrient pollution, and the risk of eutrophication and fish kill 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 because of an increased denitrification rate and pathogen decay rate with warming. The risk of eutrophication and fish kill is found to increase by 43.5 and 15% due to climate change alone by mid-twenty-first century. Our findings could be helpful to planners in water resource management to take necessary actions to improve the water quality of the Ganga River in this century.

Identification of Suitable Locations in a Small Water Supply Network for the Placement of Water Quality Sensors Based on Different Criteria under Demand-Driven Conditions

Drinking water quality monitoring in real time is of utmost importance to ensure public health. Although water utilities, following the related legislative framework, monitor drinking water quality through samplings, the likelihood of detecting contaminants in consumers’ taps is low, depending on the scale of the monitoring programme. Additionally, even if the monitoring frequency is high, there is a time delay since sampling and analysis processes take some time. The selection of suitable locations for the installation of online water quality sensors is a hard task for a water utility due to the complexity of the water distribution system, the limitations of certain network junctions which are not easily accessible, and the computational burden involved. This topic has been extensively studied in recent years and sophisticated methods have been developed using optimization techniques. However, small water utilities do not have the means to implement such tools. This paper applies a methodology to identify the suitable junctions for the installation of online water quality sensors based on different objectives and under demand-driven conditions. This paper utilizes the hydraulic simulation model of a standard network to set up the water quality simulation model. A thorough analysis of various contamination scenarios takes place with different injection nodes and at different starting injection times for 24 h. The latter relates to the contaminant’s spread due to varying water demand. After a thorough analysis of 816 scenarios, a prioritized list of the most suitable nodes for the installation of the sensors is available for each optimization objective. Comparing the prioritized list of nodes achieved from each single or multi-objective function, the detection probability is almost the same. The analysis revealed that, due to varying water demand conditions, the ranking of the proposed nodes suitable for the installation of water quality monitoring sensors differs. Thus, varying hourly water demand should be part of analyses seeking to get reliable results.

Study of Temporal and Spatial changes in the Estuarine Area of the Mayangan River, West Java – Indonesia

The estuarine of the Mayangan River is an area confluence the Mayangan River with the Java Sea. Pondok Bali beach which is about a half kilometer from River mouth of the Mayangan River has undergone a severe abrasion process since 2006. Pondok Bali’s coastline changes are expected to affect the estuary area. It caused flooding and changes in water quality on the Mayangan water river area. This research was studied the factors that cause of river level rise and changes in water quality of the Mayangan River whether it has an impact of coastline geometry change. The results of hydrodynamic and water quality model simulations showed river water discharge and increase Sea level is a more influential factor compared to the change in coastline geometry or bathymetry of the river mouth. The increase in salinity at some observation points of the Mayangan River from 2006 to 2014 was only 0.2 ppt, from 2011 to 2014 the salinity increase was only 0.1 ppt, and from 2014 to 2020 only 0.03 ppt.

Evaluation of different management scenarios for trout farm effluents using dynamic water quality modeling

Trout farm effluents are among the important sources of water pollution by nutrients, organics, and suspended solids. This research study was conducted to investigate and manage the impacts of trout farm effluents on Esen Stream water quality, which is located at the southwest of Turkey. A water quality monitoring program was conducted for a period of 1year at 21 stations to determine river hydraulics and water quality characteristics. Monthly water quality analyses were conducted for inflow and outflow of a number of selected farms to determine their characteristics and contribution to water pollution. About 1460 t of suspended solids, 230 t of nitrogen, and 46 t of phosphorus were annually discharged into Esen Stream from the farms. A dynamic water quality simulation model developed by the US-Environmental Protection Agency, WASP8, was applied to Esen Stream to predict and manage pollution from trout farms and the tributaries. The calibrated and verified model was used to evaluate the impacts of different management scenarios on stream water quality. Solid removal with a rotating micro-screen, establishment of a partially recirculated system and increasing production capacities of farms were among the simulated scenarios. The best management practice was the combined application of solids removal and reuse of treated farm effluents in a partially recirculated system, which provided significant improvements in the receiving stream water quality. This study showed that dynamic water quality models are powerful tools for basin-scale management of land-based aquaculture for capacity planning, assimilation capacity estimation, sustainable operation, and environmental impact assessment.

Significance of using dynamic land-use data and its threshold in hydrology and water quality simulation models.

Land-use changes have a significant impact on the hydrological cycle and non-point source (NPS) pollution discharge and transport. Thus, using dynamic land-use inputs in the simulation models is important. However, there is currently no clear standard for which situation the land-use data should be updated in the models. In this study, we quantified the impacts of land-use change on hydrological and NPS pollution simulation outputs, and analyzed the thresholds for land-use change level and time nodes. The results indicated that the error caused by land-use change had a linear relationship with the land-use change level. The total nitrogen (TN) output error was the most sensitive to land-use change, with a gradient of 0.73. The impact of land-use change on the model outputs was different at different temporal scales. Flow and TN had the highest output errors at a daily scale, while sediment had the highest output error at an annual scale. The threshold analysis results revealed that the land-use change thresholds for the flow, sediment, and TN simulations were 40%, 30%, and 10%, respectively. When the land-use change level exceeded the threshold, the model simulation error increased dramatically. The land-use change time node would also affect the simulation performance, especially for TN. This study initially explored the quantified standard for land-use data updates in the SWAT model. The results could be useful for improving the simulation accuracy of the SWAT model and may provide ideas for follow-up studies.

Analytical Solutions to Conservative and Non-Conservative Water Quality Constituents in Water Distribution System Storage Tanks

Water storage tanks are one of the primary and most critical components of water distribution systems (WDSs), which aim to manage water supply by maintaining pressure. In addition, storage provides a surplus source of water in case of an emergency. To gain the mentioned advantages, storage tanks are incorporated in most WDSs. Despite these advantages, storage can also pose negative impacts on water quality, thereby affecting water utilities. Water quality problems are a result of longer residency times and inadequate water mixing. This study aimed to construct a model of a tank’s water quantity and quality by formulating and solving governing equations based on inlet/outlet configurations and processes that influence the movement of water and chemical substances inside it. We used a compartment model to characterize the mixing behavior inside a tank. A water quality simulation model with different compartment arrangements was explored for extended filling and draining of storage, which was further validated using a previously published case study.

Water quality–quantity management in river–reservoir system using sustainability‐based simulation‐optimization meta‐model approach

AbstractThe present research, for the first time, introduces a sustainability‐based water quality–quantity management model in the river–reservoir system. Vulnerability, resiliency, reliability and maximum deficit are the factors that have been considered in the Sustainability Index (S.I.). The two‐dimensional hydrodynamics and water quality simulation model (CE‐QUAL‐W2) is linked to the multi‐objective particle swarm optimization (MOPSO) to develop a simulation‐optimization approach. Also, artificial neural network (ANN) model, substituted for the Ce‐QUAL‐W2 model, reduces the computational time in adaptive dynamically refined routine. The proposed methodology is applied to the Karkheh river–reservoir in Iran. The results showed that using ANN in an adaptive form, replacing CE‐QUAL‐W2, significantly impacts the computational time, considering accuracy in the developed simulation‐optimization model. Moreover, using the S.I. greatly reduces both the severity of failure periods in the water quantity–quality and the sequence of failure periods.

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.