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The main objective of the Water Framework Directive in the European countries is to achieve a \"good status\" of all the water bodies, in the integrated management of river basins. In order to assess the impact of improvement measures, water quality models are necessary. During the previous decades the progress in computer technology and computational methods has supported the development of advanced mathematical models for pollutant transport in rivers and streams. This book is intended to provide the fundamental knowledge needed for a deeper understanding of these models.

Water quality modelling efforts are designed to provide an understanding of watershed conditions to support management efforts that include control of point and nonpoint sources (NPS). Nitrogen (N) and phosphorus (P) speciation is an important area of nutrient research, both in terms of biodegradability in wastewater treatment and bioavailability in the water environment. Water quality modelling may not be reflecting all that is known about point source effluent N and P from treatment facilities that reduce the total amount of nutrients discharged and also change N and P speciation and reduce bioavailability. WERF research into advanced levels of nutrient removal treatment is revealing new information about N and P speciation and reduced bioavailability of the N and P remaining after advanced treatment.

Discharge of organic waste results in high nutrient pollution of the water bodies which is a major menace to the environment. A high quantity of nutrients such as ammonia causes a reduction in the dissolved oxygen level and induces algal growth in the water bodies. Water quality models have been the tools to evaluate the rate at which streams can disperse the pollutants they receive. Many water quality models are flawed either because of their inadequacy to completely simulate the advection component of the pollutant transport, or because of the limited application of the models, due to inaccurate estimation of model parameters. The hybrid cell in series (HCIS) developed by Ghosh et al. ( 2004 ) has been able to overcome such difficulties associated with the mixing cell-based models. Thus, the current study focuses on developing an analytical solution for the pollutant transport of the ammonia concentration through the plug flow, the first and second well-mixed cells of the HCIS model. The HCIS model coupled with the first order kinetic equation for ammonia nutrient was developed to simulate the ammonia pollutant concentration in the water column. The ammonia concentration at various points along the river system was assessed by considering the effects of the transformation of ammonia to nitrite, the uptake of ammonia by the algae, the respiration rate of the algae and the input of benthic source to the ammonia concentration in the water column. The proposed model was tested using synthetic data, and the HCIS-NH 3 model simulations for spatial and temporal variation of ammonia pollutant transport were analysed. The simulated results of the HCIS-NH 3 model agreed with the Fickian-based advection-dispersion equation (ADE) for simulating ammonia concentration solved using an explicit finite difference scheme. The HCIS-NH 3 model also showed a good agreement with the observed data from the Umgeni River, except during rainy periods.

This reference gets you up to speed on mathematical modeling for environmental and water resources management. With a practical, application-oriented approach, it discusses hydrodynamics, sediment processes, toxic fate and transport, and water quality and eutrophication in rivers, lakes, estuaries, and coastal waters. A companion CD-ROM includes a modeling package and electronic files of numerical models, case studies, and model results. This is a core reference for water quality professionals and an excellent text for graduate students.

The primary reference for the modeling of hydrodynamics and water quality in rivers, lake, estuaries, coastal waters, and wetlands This comprehensive text perfectly illustrates the principles, basic processes, mathematical descriptions, case studies, and practical applications associated with surface waters. It focuses on solving practical problems in rivers, lakes, estuaries, coastal waters, and wetlands. Most of the theories and technical approaches presented within have been implemented in mathematical models and applied to solve practical problems. Throughout the book, case studies are presented to demonstrate how the basic theories and technical approaches are implemented into models, and how these models are applied to solve practical environmental/water resources problems.  This new edition of Hydrodynamics and Water Quality: Modeling Rivers, Lakes, and Estuaries has been updated with more than 40% new information. It features several new chapters, including one devoted to shallow water processes in wetlands as well as another focused on extreme value theory and environmental risk analysis. It is also supplemented with a new website that provides files needed for sample applications, such as source codes, executable codes, input files, output files, model manuals, reports, technical notes, and utility programs. This new edition of the book: * Includes more than 120 new/updated figures and 450 references * Covers state-of-the-art hydrodynamics, sediment transport, toxics fate and transport, and water quality in surface waters * Provides essential and updated information on mathematical models * Focuses on how to solve practical problems in surface waters—presenting basic theories and technical approaches so that mathematical models can be understood and applied to simulate processes in surface waters Hailed as \"a great addition to any university library\" by the Journal of the American Water Resources Association (July 2009), Hydrodynamics and Water Quality, Second Edition is an essential reference for practicing engineers, scientists, and water resource managers worldwide.

Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of concern as it deteriorating the water quality, making it unfit for any type of use. Recently, water quality modelling using machine learning techniques has generated a lot of interest and can be very beneficial in ecological and water resources management. However, they suffer many times from high computational complexity and high prediction error. The good performance of a deep neural network like long short-term memory network (LSTM) has been exploited for the time-series data. In this paper, a deep learning–based Bi-LSTM model (DLBL-WQA) is introduced to forecast the water quality factors of Yamuna River, India. The existing schemes do not perform missing value imputation and focus only on the learning process without including a loss function pertaining to training error. The proposed model shows a novel scheme which includes missing value imputation in the first phase, the second phase generates the feature maps from the given input data, the third phase includes a Bi-LSTM architecture to improve the learning process, and finally, an optimized loss function is applied to reduce the training error. Thus, the proposed model improves forecasting accuracy. Data comprising monthly samples of different water quality factors were collected for 6 years (2013–2019) at several locations in the Delhi region. Experimental results reveal that predicted values of the model and the actual values were in a close agreement and could reveal a future trend. The performance of our model was compared with various state of the art techniques like SVR, random forest, artificial neural network, LSTM, and CNN-LSTM. To check the accuracy, metrics like root mean square errors (RMSE), the mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) have been used. Experimental analysis is carried out by measuring the COD and BOD levels. COD analysis reveals the MSE, RMSE, MAE, and MAPE values as 0.015, 0.117, 0.115, and 20.32, respectively, for the Palla region. Similarly, BOD analysis indicates the MSE, RMSE, MAE, and MAPE values as 0.107, 0.108, 0.124, and 18.22, respectively. A comparative analysis reveals that the proposed model outperforms all other models in terms of the best forecasting accuracy and lowest error rates.

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.

Nowadays, declining water quality is a significant concern for the world because of rapid population growth, agricultural and industrial activity enhancement, global warming, and climate change influencing hydrological cycles. Assessing water quality becomes necessary by using a suitable method to reduce the risk of geochemical contaminants. Water’s physical and chemical properties are compared to a standard guideline to determine its quality. The water quality index (WQI) model is a commonly helpful technique for evaluating surface and groundwater quality. The model mainly employs aggregation techniques to diminish large amounts of data to a sole value. The WQI model has been used across the globe to assess ground and surface water using regional standards. The model has become popular for its ease of use and general structure. Typically, WQI models include five stages: (1) choosing water quality indicators, (2) generating sub-parameters for each variable, (3) calculating variable weighting numbers, (4) aggregating sub-parameters to finding the total WQI value, and (5) classification of WQI value to highlight the category of water quality. In addition, the model creates ambiguity when converting vast volumes of data into a single value. The study considered 2011–2021 blinded peer-reviewed articles and book chapters to assess WQI models and their application in evaluating drinking water quality. This study mainly concentrated on the comparison of WQI models and their applications. The study also focused on the selection of parameters and problems associated with the accuracy of the models.