Search

Water quality modelling is a critical tool for managing the health of river ecosystems, particularly in regions impacted by point source pollution activities. This study investigates the influence of different hydrodynamic data sources on the performance of two river water quality models, the Basic Model (BM) and the Water Quality Analysis Simulation Programme (WASP) for modelling nitrogenous compounds in a complex river system including wastewater treatment plant effluent discharges. Four diverse hydrodynamic data input types were considered. These included measured station data, altered station data, rainfall-generated flow, and the WRSM/Pitman model estimate. Findings revealed trends, analysis of variance (ANOVA), and t-test analyses consistently demonstrated significant disparities between model predictions and measured data in specific river segments, indicating a need for segment-specific modelling approaches. An increase in Root Mean Square Error (RMSE) and Mean Square Error (MSE) values in certain segments pointed to a decline in model accuracy when confronted with distinct hydrodynamic conditions. Additionally, application of four diverse hydrodynamic data input sources yielded similar performance for BM and WASP against measured data. The research findings indicated a complex interplay between river hydrodynamics and water quality modelling, resulting in a recommendation for tailored modelling strategies that account for unique characteristics of river segments. HIGHLIGHTS Hydrodynamic data input sources yielded similar performance for the Basic Model and WASP against measured data for nitrogenous compounds.; Reduced performance of models farther from boundary was detected.; Altered station hydrology showed comparable impact on WASP and Basic Model.; Examination of segment-specific model accuracy disparities across varied inputs and models.;

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River's water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the results of water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability. [ABSTRACT FROM AUTHOR]

Rapid industrial development is one of the leading causes of environmental degradation. The textile industries and the domestic activities in Majalaya District produce wastewater directly discharged into the Cikakembang River. As a result, the Cikakembang River’s water quality has decreased to the point that the water quality cannot be used for daily needs. This study modeled three main parameters in water quality modelling, namely Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). Using MATLAB, the three-water quality governing equations originating from the Advection-Dispersion Equation were solved using the Runge Kutte-4 discretization scheme. The numerical modelling was carried out along 2.36 km of the Cikakembang River. All water quality coefficients, such as the DO Saturation (DOsat), the Reaeration Rate (ka), the Dispersion Coefficient (D), the Deoxygenation Rate (kd), and the Decomposition Rate (kc), for the Cikakembang River were estimated using equations developed by existing studies. The estimation of ka and D coefficients requires hydraulic parameters, which in this study were estimated using the HEC-RAS simulation. Meanwhile, kd and kc values were obtained from the calibration and verification process. The Relative Root Mean Square Error (RRMSE) objective function was used to evaluate the results of water quality modelling at three sampling points. In the calibration process, the results of water quality modelling produced RRMSE values for the DO, BOD, and COD parameters of 1.99%, 0.36% and 0.92%, respectively. Meanwhile, for the verification process, the RRMSE values for the DO, BOD, and COD parameters are 1.95%, 1.02% and 1.86%. All water quality parameters produce small RRMSE values in the calibration and verification processes. Hence, the water quality model created has good accuracy and stability.

The modelling of solid transport in open channels requires good knowledge about parameters related to basic processes such as hydrodynamic dispersion, advection and decay rates. Such parameters are usually determined by dye tests. Numerous tracer studies have been performed on laboratory flumes and natural rivers. However, on-site sampling is often difficult, expensive and needs special apparatus. The main aim of the study was to justify simplified method based on the monitoring of the dye cloud shape in order to determine both longitudinal and transversal dispersion coefficients. In this study, four dye tests were carried out on a small local stream (the Lipkovsky) using Rhodamine WT fluorescein dye as a tracer. The tests were carried out in such a manner that both longitudinal and horizontal transversal dispersion data were obtained. For this purpose, the visually determined extent of the dye cloud was interpreted via the analytical solution of the advection-dispersion equation. The results obtained by this simplified approach indicated that the longitudinal dispersion coefficient Dx = 0.051–0.057 m2/s and the coefficient of horizontal transversal dispersion Dy = 0.00024–0.00027 m2/s. The method was justified by corresponding root mean square error (RMSE) counting RMSE = 0.65–1.02 m for the dye cloud centre, RMSE = 1.87–2.46 m for the head and tail of the cloud and RMSE = 0.025–0.11 m for the cloud width, the Nash-Sutcliffe efficiency coefficients ranged from 0.9 to 0.998. The comparison of these values with empirical formulae and other tracer studies indicated significant overestimation of the mentioned values of Dx, which can be attributed to the uniform velocity distribution along the width of Lipkovsky Stream. Much better agreement was achieved for Dy.

Water quality monitoring and modeling are vital in improving the aquatic ecosystem's health and surroundings. The advancements in computer science and its integration with mathematics have resulted in the development of divergent algorithms and models for evaluating/predicting water quality and simulating the fate/transport of environmental contaminants. In this paper, four widely used statistical methods/algorithms, viz. (1) topological method, (2) multivariate statistics, (3) geostatistics, and (4) information entropy method, have been discussed and assessed. The assessment is based on its application merits and de-merits in recent environmental/water resources projects to advocate its suitability and flexibility in water quality analysis. The assessment parameters of suitability taken into account are pollutant source identification and classification. The reviewed methods argue for river water quality improvement through restoration and pollution control plans, simultaneously trying to minimize the number of sampling locations. Further, the five most widely used WQ models, viz. MIKE, AQUATOX, SWAT, IBER, and TELEMAC have been compared based on their mode of access (paid/freely available), input data requirement, output, and applicability for specific scenarios (e.g., oil spillage, contaminant transport, etc.). This paper is the first of its kind that compares and reviews IBER software and other water quality modeling/analysis software. The review is constructed to guide the reader in selecting a particular method and software/model in various scenarios. The study of the water quality models will also help in selecting the most accurate model to uncover the distribution of biochemical contaminants in a water body and its prediction to generate risk maps. [ABSTRACT FROM AUTHOR]

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, basic interpolation and machine learning data augmentation were applied to scarce data used in Water Quality Analysis Simulation Programme (WASP) and Continuous Stirred Tank Reactor (CSTR) that were applied to nitrogenous compound degradation modelling in a river reach. Model outputs were assessed for statistically significant differences. Furthermore, artificial data gaps were introduced into the input data to study the limitations of each augmentation method. The Python Data Analysis Library (Pandas) was used to perform the deterministic interpolation. In addition, the effect of missing data at local maxima was investigated. The results showed little statistical difference between deterministic interpolation methods for data augmentation but larger differences when the input data were infilled specifically at locations where extrema occurred. HIGHLIGHTS Basic interpolation methods did not produce statistically significant differences in augmented datasets.; Increasing the gaps yielded greater differences between augmented datasets.; ML methods on real and artificial gaps produced acceptable results.; No significant differences between the WASP and Basic Model on real and artificial input.; Difference between the WASP and Basic Model on real and artificial input.;

Background: Water quality evaluation require arduous laboratory and statistical analyses comprising of sample collection and sometimes transportation to laboratories, which may be expensive. In recent years, there has been an emergent need to monitor the dissolved oxygen (DO) concentrations of Kubanni reservoir as a result of anthropogenic and agricultural pollution. Hence, this study was conducted to apply adaptive neuro-fuzzy inference system (ANFIS)-based modelling in the prediction of DO of Kubanni reservoir. Methods: Water quality data for seven years were used to develop ANFIS models. Six water quality parameters, namely, total dissolved solids, free carbon dioxide, turbidity, temperature, manganese, and electrical conductivity, were selected for analysis based on their sensitivity. Subtractive clustering and grid partitioning techniques were considered when generating the fuzzy inference system (FIS). Three ANFIS models according to different lengths for training data and testing data were selected for modelling. Results: The results showed that Model-1 gave the best correlation (R-squared and adjusted R-squared of 0.852503 and 0.845000, respectively) for whole data using six input variables. While Model-3 gave the best correlation (R-squared and adjusted R-squared of 0.807791 and 0.799940, respectively) for whole data using three input variables. Conclusion: The performance efficiency of ANFIS model 1 using 6 inputs shows that the model is reliable for modelling water quality.

South African rivers generally receive waste from inadequate wastewater infrastructure, mines, and farming activities, among others. The uMsunduzi River in KwaZulu-Natal, South Africa, is among these recipients with recorded poor to very poor water quality. To identify parts of the uMsunduzi River that are polluted by Cryptosporidium and Escherichia coli (E. coli), this study mapped out pollutants emanating from point and non-point sources using the Soil and Water Assessment Tool (SWAT). Streamflow calibration in the upper and lower reaches of the catchment showed good performance with R2 of 0.64 and 0.58, respectively. SWAT water quality output data were combined with a Quantitative Microbial Risk Assessment (QMRA) to understand the microbial health implications for people using river water for drinking, recreational swimming, and non-competitive canoeing. QMRA results for Cryptosporidium and pathogenic E. coli showed that the probability of infection for most users exceeds the acceptable level for drinking and recreation as outlined in the South African water quality guidelines, and by the World Health Organization (WHO). The results of this study can be used as a baseline to assess the economic and health implications of different management plans, resulting in better-informed, cost-effective, and impactful decision-making. HIGHLIGHTS SWAT model identified areas of the uMsunduzi River that were polluted by Cryptosporidium and E. coli.; SWAT water quality output data were used in QMRA.; QMRA investigated the impacts of river water on canoeists, recreational swimmers, and those who drink the water.; uMsunduzi River water is not suitable for drinking and recreation according to QMRA results.; These results can inform policies and decision-making within the catchment.;

• The Coastal Generalized Ecosystem Model (CGEM) is an open-source model. • CGEM offers different formulations for rate processes, providing a flexible model structure. • Model formulations have significant effects on simulation outcomes. • Flexible model structure is important for adapting models to different ecosystems. • Formulations impact simulation outcomes for climate change and HABs. The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues. [ABSTRACT FROM AUTHOR]

The EPANET model is commonly used to model hydraulic behaviour and water quality within water distribution networks. The standard version of the model solves the advective transport equation by solving a mass balance of the fundamental plug flow substance that considers the advective transport and kinetic reaction processes. Over the years, several versions of the model have been developed, which have made it possible to improve the modelling of water quality through the introduction of additional terms within the transport equation to solve the problem of dispersive transport (EPANET-AZRED) and to consider multiple interacting species in the mass flow and on the pipe walls (EPANET multi-species extension). The present study proposes a novel integration of the EPANET-DD (dynamic-dispersion) model, which enables the advective–diffusive–dispersive transport equation in dynamic flow conditions to be solved in the two-dimensional case, through the classical random walk method, implementing the diffusion and dispersion equations proposed by Romero-Gomez and Choi (2011). The model was applied to the University of Enna "KORE" laboratory network to verify its effectiveness in modelling diffusive–dispersive transport mechanisms in the presence of variable flow regimes. The results showed that the EPANET-DD model could better represent the actual data than previously developed versions of the EPANET model. [ABSTRACT FROM AUTHOR]

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method.

Propelled by the rapid development of equipment, technology and computational power, the monitoring and simulation of the hydrodynamics in lakes have steadily advanced. In contrast, water quality simulations are more difficult to implement, due to the difficulty in obtaining large-scale, spatially resolved field observations for model validation and the number of interacting processes to be parameterized. Here we demonstrate that remote sensing data can be used to inform Lagrangian particle tracking in a large lake, and vice versa. We used total suspended matter (TSM) as a parameter that can be both estimated from the backscattering in satellite images and modelled in terms of particle abundance. Specifically, we compared TSM concentrations in Lake Geneva deduced from images taken by Sentinel-2 and Sentinel-3 satellites to those estimated from Delft3D hydrodynamic and particle tracking models. TSM concentrations obtained from both methods were compared over a time span of up to 5 days in several scenario studies, including instantaneous and continuous point sources and large-scale TSM simulations. The results demonstrate that remote sensing images can serve to calibrate and validate particle tracking models with independent observations. The model was able to capture both the position of a TSM cloud arising 5 days after an instantaneous point source release, and the direction of particle transport and TSM plume size resulting from a continuous source. Even when simulating the whole lake domain, model results closely approximated the satellite-derived TSM concentrations along lake transects within 9%. In return, the particle tracking model was able to complete partially impaired satellite images, and fill in a four-day image gaps between satellite revisits. The synergy of remote sensing techniques and particle tracking modelling allows a rapid, continuous and more accurate analysis on solute transport in lakes.

South African rivers generally receive waste from inadequate wastewater infrastructure, mines, and farming activities, among others. The uMsunduzi River in KwaZulu-Natal, South Africa, is among these recipients with recorded poor to very poor water quality. To identify parts of the uMsunduzi River that are polluted by Cryptosporidium and Escherichia coli (E. coli), this study mapped out pollutants emanating from point and non-point sources using the Soil and Water Assessment Tool (SWAT). Streamflow calibration in the upper and lower reaches of the catchment showed good performance with R² of 0.64 and 0.58, respectively. SWAT water quality output data were combined with a Quantitative Microbial Risk Assessment (QMRA) to understand the microbial health implications for people using river water for drinking, recreational swimming, and non-competitive canoeing. QMRA results for Cryptosporidium and pathogenic E. coli showed that the probability of infection for most users exceeds the acceptable level for drinking and recreation as outlined in the South African water quality guidelines, and by the World Health Organization (WHO). The results of this study can be used as a baseline to assess the economic and health implications of different management plans, resulting in better-informed, cost-effective, and impactful decision-making. [ABSTRACT FROM AUTHOR]

The Cikapundung river basin community uses the Cibarani channel as a drainage system and water source for fishing. However, the test result released on 9th November 2020 revealed that the channel’s water quality failed to reach the class II raw water standards due to various domestic waste discharges. This led to the performance of various studies to identify pollution control techniques by limiting the wastewater discharge and quality, controlling the intake discharge, and using baffles. The Cibarani channel has a drop-structure that can improve the water quality, though the effect has not been previously detailed. Therefore, this study was intended to comprehensively examine the effect of the drop-structure along the Cibarani channel to improve water quality conditions, specifically the Dissolved Oxygen (DO) parameter. This study employed the one-dimensional HEC-RAS software to simulate the hydrodynamic and water quality conditions along the Cibarani channel, and the drop-structure was modelled using two alternatives consisting of a vertical wall and a steep riverbed. Subsequently, the drop-structure fitted with a vertical wall gave a more plausible reaeration rate of 125 day-1 and Root Mean Square Error (RMSE) value of 0.50. The placement of a similar configuration before the first housing of the channel increased the DO concentrations by an average of 4.37 mg/L. This was followed by the modelling of another drop-structure after the first housing to increase the DO levels at the downstream part. Eventually, the combination of the two new drop-structures succeeded in increasing the DO concentrations along the Cibarani channel to 3.3 - 6.9 mg/L.

In the discussion of water quality control, the first and most effective parameter that affects other variables and water quality parameters is the temperature situation and water temperature parameters that control many ecological and chemical processes in reservoirs. Additionally, one of the most important quality parameters studied in the quality of water resources of dams and reservoirs is the study of water quality in terms of salinity. The salinity of the reservoirs is primarily due to the rivers leading into them. The control of error in the reservoirs is always considered because the outlet water of the reservoirs, depending on the type of consumption, should always be standard in terms of salinity. Therefore, in this study, using the available statistics, the Ce-Qual-W2 two-dimensional model was used to simulate the heat and salinity layering of the Latyan Dam reservoir. The results showed that with warming and shifting from spring to late summer, the slope of temperature changes at depth increases and thermal layering intensifies, and a severe temperature difference occurs at depth. The results of sensitivity analysis also showed that by decreasing the wind shear coefficient (WSC), the reservoir water temperature increases, so that by increasing or decreasing the value of this coefficient by 0.4, the average water temperature by 0.56°C changes inversely, and the results also show that by increasing or decreasing the value of the shade coefficient by 0.85, the average water temperature changes by about 7.62°C, directly. [ABSTRACT FROM AUTHOR]

This paper studies the current status of water quality, using both statistical and modelling approaches, in the waterways of the Saigon-Dong Nai river basin in relation to the nature of its environment and the effect of point and diffuse pollution sources. These sources are located mainly in the urban and industrial areas of Ho Chi Minh City (HCMC) and Dong Nai province. The statistical analysis of monitoring data allows us to determine the state of water quality while the modelling approach provides the means to manage the pollution sources. Both approaches are used in this work. Our statistical analysis of the water quality data collected at many stations from the upper reach of the Saigon and Dong Nai rivers to the urban waterways and the lower end of the rivers near the sea shows a contrast of water quality in different basin areas. These differences are consistent in both dry and rainy seasons and during the high tide and low tide. The results show that several hot spots of some water quality variables require management focus, such as organic waste water pollution at Binh Dien and Thay Cai in HCMC mainly due to local sources. Moderately and highly degraded water quality occurs at Binh Phuoc, Phu An and Cat Lai which are downstream from the industrial areas of Binh Duong (Binh Duong province) and Bien Hoa (Dong Nai province) before entering HCMC. Modelling of some important water quality variables also confirms the spatial extent of the areas where water quality is different as analysed from the statistical analysis of monitoring data. The MIKE-11 modelling system is shown to be a valuable tool for predicting the effect of different sources in HCMC metropolitan area on the waterway areas where water quality is important to protect the ecosystem and people health. [ABSTRACT FROM AUTHOR]

Faecal Indicator Organism (FIO) concentrations in nearshore coastal waters may lead to significant public health concerns and economic loss. A three-dimensional numerical source-receptor connectivity study was conducted to improve the modelling of FIO transport and decay processes and identify major FIO sources impacting sensitive receptors (source apportionment). The study site was Swansea Bay, UK and the effects of wind, density, and tracer microbe (surrogate FIO) decay models were investigated by comparing the model simulations to microbial tracer field studies. The relevance of connectivity tests to source apportionment was demonstrated by hindcasting FIO concentration in Swansea Bay with the identified FIO source and the Impulse Response Function (IRF) in Control System theory. This is the first time the IRF approach has been applied for FIO modelling in bathing waters. Results show the importance of density, widely ignored in fully mixed water bodies, and the potential for biphasic decay models to improve prediction accuracy. The microbe-carrying riverine freshwater, having a smaller hydrostatic pressure, could not intrude on the heavier seawater and remained in the nearshore areas. The freshwater and the associated tracer microbes then travelled along the shoreline and reached bathing water sites. This effect cannot be faithfully modelled without the inclusion of the density effect. Biphasic decay models improved the agreement between measured and modelled microbe concentrations. The IRF hindcasted and measured FIO concentrations for Swansea Bay agreed reasonably, demonstrating the importance of connectivity tests in identifying key FIO sources. The findings of this study, namely enhancing hydro-epidemiological modelling and highlighting the effectiveness of connectivity studies in identifying key FIO sources, directly benefit hydraulics and water quality modellers, regulatory authorities, water resource managers and policy. [Display omitted] • Effects of density and microbe decay on water quality modelling were investigated. • Numerical source-receptor connectivity study was conducted for a coastal site. • Density effect causes freshwater and microbe to remain in nearshore areas. • Biphasic decay models were shown to improve microbe prediction accuracy. • Impulse Response Function (IRF) could be used to identify key microbial sources. [ABSTRACT FROM AUTHOR]

The EPANET model is commonly used to model hydraulic behaviour and water quality within water distribution networks. The standard version of the model solves the advective transport equation by solving a mass balance of the fundamental plug flow substance that considers the advective transport and kinetic reaction processes. Over the years, several versions of the model have been developed, which have made it possible to improve the modelling of water quality through the introduction of additional terms within the transport equation to solve the problem of dispersive transport (EPANET-AZRED) and to consider multiple interacting species in the mass flow and on the pipe walls (EPANET multi-species extension). The present study proposes a novel integration of the EPANET-DD (dynamic-dispersion) model, which enables the advective–diffusive–dispersive transport equation in dynamic flow conditions to be solved in the two-dimensional case, through the classical random walk method, implementing the diffusion and dispersion equations proposed by Romero-Gomez and Choi (2011). The model was applied to the University of Enna “KORE” laboratory network to verify its effectiveness in modelling diffusive–dispersive transport mechanisms in the presence of variable flow regimes. The results showed that the EPANET-DD model could better represent the actual data than previously developed versions of the EPANET model.

Surface water quality is continuously changing due to anthropogenic activities and natural causes. The drinking water treatment technology can be expensive and ineffective if implemented without identifying the patterns of parameter exceedances. The objectives of this paper were to: (i) develop a mean exceedance model; (ii) apply the model for identification of the exceeded parameters; (iii) obtain the exceedance patterns of parameters; and (iv) obtain decision making on the treatment of parameters. The mean exceedance model was developed by utilizing cluster database of 12 major Canadian rivers. The clusters were developed on the basis of normalization model, principal component analysis, total exceedance model and Canadian Water Quality Index. On application of mean exceedance model, the parameters were identified, which exceeded the water quality guidelines. The output of mean exceedance model was utilized for making decision on treatment of parameters. The normalized water quality data of 17 parameters was used to develop a mean exceedance model to obtain exceedance level for water quality parameters. The mean exceedance for the parameters increased as the cluster number increased from low to high for all the rivers. Overall, the mean exceedance was higher for fecal coliforms, turbidity, total phosphorus, total nitrogen, true colour, dissolved oxygen, iron and manganese. The exceedance in fecal coliforms, turbidity, total phosphorus, total nitrogen, true colour, dissolved oxygen could be related to anthropogenic activities of landuse/landcover (LULC). The exceedance in iron and manganese could be associated to natural mineralization. The mean exceedance model was found useful for obtaining the specific parameters with their exceedance levels. The parameter exceedance patterns could be utilized for the development of economical, efficient and targeted treatment technology for the source waters. [ABSTRACT FROM AUTHOR]

An integrated linear regression technique was developed and validated to model dissolved oxygen in salt lakes by using R software and based on data from Sawa Lake, Iraq. The technique helps understand and evaluate salty aquatic ecosystems in the presence of water quality data gaps. The technique involved selecting the important water quality parameters that have significant statistical relationship with dissolved oxygen. In order to make the regression development simpler, the validation approach was incorporated with the model. Linearity, homogeneity, normality, outliers, and influential data points were verified. The simulation approach was also capable of displaying the interaction between the selected water quality parameters and the other insignificant parameters. The statistical analysis results indicated that dissolved oxygen in salt lakes is a function of total dissolved solids. The developed model represented dissolved oxygen with R-squared of 90.73% and p-value of 1.08E-06. Furthermore, the model results showed that the influence of salty ions on dissolved oxygen/total dissolved solids model is the same. It was found that temperature has a significant impact on the developed dissolved oxygen model. In addition, the model simulation revealed that salt melting surrounding the lake due to temperature variation during the year cycle increased total dissolved solids in the lake water. Consequently, the lake dissolved oxygen levels were impacted.

Water sources pollution is a great environmental problem that negatively effects on life. The first step to prevent this environmental problem should be determined pollution by monitoring the water quality in sources. In this research, studies on the field and in the laboratory, water quality parameters of Akköprü stream located in the province of Van, were analyzed. The in situ measurements and analyses were made on water samples taken from nine different sampling points on the Akköprü stream monthly in 2018. Measurements and analysis of water quality parameters and pollution loads (ammonium, nitrite, nitrate, phosphate, chlorine, COD, DO, pH, conductivity vs.) were made and the model based on the four important variables (ammonium, nitrate, phosphate, COD) were simulated. Along the stream length, the mean absolute percentage errors were calculated to compare simulation data with the experimental data. According to the results, model predictions with experimental data has been observed that a significant degree of alignment. The important indicators of stream ammonium nitrogen, nitrate nitrogen, phosphate and chemical oxygen demand in the average absolute error values, for these variables, calculated. It is observed that the water flow regime of the stream is irregular and this is reflected in the water quality. The quality of stream water could be ranked as I and II classes and it has suitable quality concerning aquaculture and irrigation. [ABSTRACT FROM AUTHOR]

An integrated linear regression technique was developed and validated to model dissolved oxygen in salt lakes by using R software and based on data from Sawa Lake, Iraq. The technique helps understand and evaluate salty aquatic ecosystems in the presence of water quality data gaps. The technique involved selecting the important water quality parameters that have significant statistical relationship with dissolved oxygen. In order to make the regression development simpler, the validation approach was incorporated with the model. Linearity, homogeneity, normality, outliers, and influential data points were verified. The simulation approach was also capable of displaying the interaction between the selected water quality parameters and the other insignificant parameters. The statistical analysis results indicated that dissolved oxygen in salt lakes is a function of total dissolved solids. The developed model represented dissolved oxygen with R-squared of 90.73% and p-value of 1.08E-06. Furthermore, the model results showed that the influence of salty ions on dissolved oxygen/total dissolved solids model is the same. It was found that temperature has a significant impact on the developed dissolved oxygen model. In addition, the model simulation revealed that salt melting surrounding the lake due to temperature variation during the year cycle increased total dissolved solids in the lake water. Consequently, the lake dissolved oxygen levels were impacted. [ABSTRACT FROM AUTHOR]

Abstract Eutrophication of lakes has affected society in many regions, particularly in water scarce environments where: i) low runoff reduces the self-purification potential of water bodies; ii) water supply relies on surface reservoirs, which are susceptible to nutrient enrichment. This work presents an assessment of the impact of the silted sediment management on the trophic status of a tropical surface reservoir with intense temporal variability of water storage. A complete mixing model describing the total phosphorus budget in the water and sediments was used, based on semi-empirical formulations. The sediment reuse as soil fertilizer has been proposed to increase productivity in small scale agriculture, which should also enhance the water quality by removing the nutrient-enriched sediment from lakes. Model application for a 40-years period indicate that sediment management may improve water quality, changing from poor to acceptable trophic state during roughly 10% of the time when the reservoir is not empty.

The use of adequate decay models for simulating chlorine residuals can effectively aid in chlorine management in water supply systems. In this paper, wall decay in a full-scale water supply system is assessed and modelled using the traditional first-order (FO) model and the recent EXPBIO model. The EXPBIO model was successfully implemented in EPANET-MSX for the first time and predicted chlorine residuals with high accuracy. However, in the tested conditions (chlorine residuals ≥0.55 mg/L and small wall decay rates), the FO and the EXPBIO models described chlorine wall decay with similar accuracy. The results suggest that in systems of large diameter pipes and of high disinfectant concentrations, the simpler FO model can be used for the modelling of chlorine residuals without significant loss of accuracy. Further research is needed to identify in which conditions (chlorine levels, wall decay rates) the EXPBIO model performance may exceed that of the FO model. [ABSTRACT FROM AUTHOR]

The objective of the research is to find low cost alternative for conventional recreational lagoons that consume water and energy used for desalination which is the only alternative for water treatment in most touristic villages all over the world. The study uses low cost recreational lagoon with new technology that use brackish water from deep wells and purify this water before entering the lagoon by controlled pulses and energy-efficient ultrasound filtration. This allows to maintain the water within pre-defined parameters, guaranteeing standardized water quality in all lagoons. The research introduces the lagoon new technology and its low cost design including feeding and drainage wells, second, the hydrographic surveying for the coastline in the study area, third water quality modelling for the production and injection wells, fourth, use SOBEK 1-2 Mathematical Model for determine the water depth and perspective water volume for the designed lagoon. The aim of this model: Determine the relation between the water depth and the water volume for the canal and the lakes. Second, calculate the evaporation rate from the surface, Determine the number and capacity of the water wells needed to fill the canal and the lakes, and Find out the relationship between the discharge and the time needed to circulate the water in the canal and the lakes to keep their water quality. The results of the measurements from the observation well prove that the optimal discharge per each well is 0.022 m³·s-1. The construction of suggested new green technology lagoon are very low cost, completely environmentally friendly, in addition fulfils the highest standards of environmental safety. [ABSTRACT FROM AUTHOR]

Accurate estimation of solute transport has significant importance in water resources and environmental engineering. Among the various types of mathematical formulation for modeling of solute transport, the transient storage model has been widely applied by different researchers as an appropriate model. In this study, an improved version of symbiotic organism search (SOS) algorithm was used to estimate the parameters of transient storage model. A large set of data from natural rivers of USA was collected from the literature and used for derivation and validation of the algorithm. The performance of the algorithm was evaluated by standard statistical indices. Accordingly, the values of R and RMSE for transient storage model parameters (Kf, T and ε) were obtained 0.922 and 30.62 (for Kf), 0.596 and 5645 (for T) and, 0.643 and 0.019 (for ε) for whole dataset. In addition, the results of this study were compared with those obtained by different reserachers via other models. The results indicate the higher capability of improved SOS algorithim compared to the others in estimating the transient storage model parameters. [ABSTRACT FROM AUTHOR]

The seasonal and interannual variations of total coliforms (TC) and Escherichia coli (EC) were investigated in an urban tropical lake. Precipitation and flow rate were the controlling parameters influencing coliform dynamics. Correlations were obtained to describe the lake inlet conditions of flow rate, TC and EC. While TC increased with flow rate, EC slightly decreased. This unexpected behaviour for EC was attributed to the counterbalancing impact of coliform loading and surface runoff dilution. Correlations were computed to obtain first-order decay rates of TC and EC as functions of the Reynolds number, assuming the plug-flow model. A general mechanistic-empirical model was proposed to simulate coliform dynamics. The results indicated that seasonal coliform variations are more pronounced than interannual variations. Considering both the plug-flow and complete-mix models, it was shown that eliminating dry weather load reduces TC and EC but still leads to exceedances of the mandatory standards during the rainy season. [ABSTRACT FROM AUTHOR]

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments. [Display omitted] • Evaluating the fate and transport of ARB and ARGs in water environments is critical to mitigating human health risks. • Presented different environmental matrices that contribute to antibiotic resistant pollution. • Discussed the critical role of environmental factors in the persistence, survival, and decay of ARB in water bodies. • Discussed the current knowledge of AR modelling, complexities of resistance mechanisms, and limitations to overcome. • Efficient water quality model development is vital to assess the emergence and spread of AR. [ABSTRACT FROM AUTHOR]

Urban lakes are often disconnected to the surrounding rivers, and they are affected by rainfall runoff pollution. This study focused on an artificial lake-river system in Shanghai featuring a spiderweb-structure, it is expected to detect whether the tributaries in such a system made a protective and buffering effect on the lake under the impact of rainfall runoff. A hydrodynamic model was established using MIKE 21, one of the most widely used software which can conduct two-dimensional modeling of water quality changes in surface water, to simulate the changes of total nitrogen (TN) and total phosphorus (TP), the key indicators causing lake eutrophication, under different rainfall events. The results showed that, under various conditions, storm events almost have no first or final effect on the central lake, TN and TP in the lake were improved by 5%–18% and 1%–12% among monitoring stations. However, Obvious negative effects were displayed in the water quality of radial – circular tributaries, and the first effects in circular tributaries were more serious than those on the radial because their difference in hydrological condition. With the increase of rainfall intensity, the peak value of the pollutant concentration in circular rivers increased 63%–185% as compared with the initial. As to final effects, some circular rivers were significantly impacted, and the pollutant concentration increased by approximately 40% in the seventh day as rainfall started. It was deduced that tributaries played as protective buffer against pollutants in this spiderweb water system, in that pollutants would be degraded in such dense spiderweb-structured river systems before entering the central lake, and rainfall runoff would also accelerate the flow in rivers and lakes. Therefore, the paper hold that, the central lake could not be separated from the radial – circular tributaries, and the officially planned pumps and sluices in the river estuaries would not be recommended. This study would provide technical support for the planning, construction and ecological protection of artificial urban lake-river system in similar areas. [Display omitted] • Effect of rainfall runoff on a spiderweb-structured water system was assessed. • The circular tributaries were more impacted than the radial ones. • The tributaries play a protective role by buffering pollutants from the lake. • Sluices were not suggested to be constructed at the estuaries. [ABSTRACT FROM AUTHOR]

This study presents an application of the SWAT model (Soil and Water Assessment Tool) in two meso-scale catchments in Poland (Upper Narew and Barycz), contrasting in terms of human pressures on water quantity and quality. The main objective was multi-variable and multi-site calibration and validation of the model against daily discharge, sediment and nutrient loads as well as discussion of challenges encountered in calibration phase. Multi-site calibration and validation gave varied results ranging from very good (daily discharge) to acceptable (sediment, nitrogen and phosphorus loads in most of gauges) and rather poor (individual gauges for all variables) in both catchments. The calibrated models enabled spatial quantification of water yield, sediment and nutrient loads, indicating areas of special concern in terms of pollution, as well as estimation of contribution of pollution from different sources, indicating agriculture as the most important source in both catchments. During the calibration process a number of significant issues were encountered: (i) global vs. local parametrization, (ii) simulation of different pools of water quality parameters in reservoirs and streams and (iii) underestimation of NO3-N loads in winter due to farmers practices. Discussion of these issues is hoped to aid SWAT model users in Poland in a deeper understanding of mechanisms of multi-variable and multi-site calibration.

The article is focused on analysis of input data impact on outputs of water quality models. The authors examined the impact of roughness coefficient, both boundary and initial conditions setup on changes of outputs generated by HEC-RAS model. Simulation results have shown a various response rate of input data on simulated results. The strong impact shows roughness coefficient setup that through the value of longitudinal dispersion coefficient affects pollution transport process. Changes in boundary conditions have had less influence on outputs. Relatively strong impact shows the setup of initial state of pollution concentration along the reach mainly for the case of low gradient rivers. [ABSTRACT FROM AUTHOR]

River water quality models usually apply the Fischer equation to determine the longitudinal dispersion coefficient (Dx) in solving the advection–dispersion equation (ADE). Recently, more accurate formulas have been introduced to determine Dx in rivers, which could strongly affect the accuracy of the ADE results. A numerical modelling-based approach is presented to evaluate the performance of various Dx formulas using the ADE. This approach consists of a finite difference approximation of the ADE, a MATLAB code and a MS Excel interface; it was tested against the analytical ADE solution and demonstrated using eight well-known Dx formulas and tracer study data for the Chattahoochee River (USA), the Severn (UK) and the Athabasca (Canada). The results show that Dx has an important effect on tracer concentrations simulated with the ADE. Comparison between the simulated and measured concentrations confirms the appropriate performance of Zeng and Huai's formula for Dx estimation. Use of the newly proposed equations for Dx estimation could enhance the accuracy of solving the ADE. [ABSTRACT FROM AUTHOR]

Abstract Recent water resources planning and management strategies state that the concepts of risk and variable inputs should be appraised in order to comply with multiple conditions. This becomes evident especially in environments with diverse uses of water, land use and climate change. In such a context, modelling of discharges and concentrations in rivers are valuable strategies to predict different scenarios. This research proposes an integrated analysis for modelling of flow and contaminant transport in rivers, based on hydrodynamics, time series, and water quality simulations. The first module estimates water volume and velocity, that have direct impact in pollutants transport; time series of concentrations are generated as synthetic pollutographs, using techniques based on flow conditions, time and statistical factors of a historical monitoring dataset – the objective is to match temporal scales of boundary conditions, since water quality data is usually available as irregular samples; the third module solves the advection-dispersion-reaction equation, exploring the different synthetic series as input. Results evidence that the input pollutograph, usually not explored in similar studies, may have a significant role in simulations for transport of substance in rivers under unsteady state; as consequence, corroborate with better estimates for planning strategies where temporal dynamic is relevant. The contributions lay the basis for further assessment of riverine systems linked to watershed dynamics, with multiple scenarios of data availability and input conditions. Graphical abstract Image 1 Highlights • Pollutant transport modelling under unsteady state is viable with limited data. • Preservation of seasonal metrics is a reasonable criterion to estimate time series. • Uncertainty in upstream boundary condition may lead to inadequate decisions. • Deterministic and empirical techniques integrate multiple uncertainties/conditions. [ABSTRACT FROM AUTHOR]

Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R² values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment. [ABSTRACT FROM AUTHOR]

Abstract It has been shown that climate change impacts the overall health of a river's ecosystem. Although predicting river health under climate change would be useful for stakeholders to adapt to the change and better conserve river health, little research on this topic exists. This paper presents a methodology predicting river health under different climate change scenarios. First, a multi-source, distributed, time-variant gain hydrological model (MS-DTVGM) was used to predict the runoff from a mountainous river in eastern China using the data from three existing IPCC5 climate change models (RCP2.6, RCP4.5, and RCP8.4). Next, a model was developed to predict the river's water quality under these scenarios. Finally, a multidimensional response model utilizing hydrology, water quality, and biology was used to predict the river's biological status and ascertain the impact of climate change on its overall health. The river is in a mountainous area near Jinan City, one of China's first "pilot" cities recognized as a "healthy water ecological community." Our results predict that the overall health of the Yufu River, which is minimally influenced by human activities, will improve by 2030 due to the increased river flow due to an increase in rainfall frequency and subsequent peak runoff. However, the total nitrogen concentration is predicted to increase, which is a potential eutrophication risk. Therefore, effective control of nitrogen pollutants entering the river will be necessary. The increase in flow velocity (the annual average increase is ~0.5 m/s) is favorable for fish reproduction. Our methods and results will provide scientific guidance for policy makers and river managers and will help people to better understand how global climate change impacts river health. Graphical abstract Unlabelled Image Highlights • We presented a methodology for predicting future river health under climate change. • A remotely sensed hydrological model was used to predict future river runoff. • We set up a water quality model to predict future water quality status. • A multidimensional response model was adopted to predict future biological status. • This study can help make wise policies for adaptation to climate change. [ABSTRACT FROM AUTHOR]