They can be used to predict changes in water quality parameters such as dissolved oxygen (DO), chemical oxygen demand (COD), biochemical oxygen demand (BOD), etc. Vn = volume of the water quality cell in the previous time step (m3 ) Vn+1 = volume of the water quality cell in the current time step (m3) Qup = upstream flow (m3/s).
Background
Biochemical oxygen demand (BOD), dissolved oxygen (DO), temperature, total dissolved solids (TDS) and chlorides are some of the important water quality indicators for pollution. Thus, there is room for continuous development of water quality models to assess water quality status.
Motivation
Both models are proposed to be used to assist in water quality management of this river by modeling some of the important water quality parameters such as BOD, COD and DO. Therefore, an attempt will be made in this study to improve the water quality modeling capabilities of the Hybrid Cells in Series model by including BOD and COD in the model.
Focus and Purpose of Study
In recent decades, various components and variables affecting water quality have gradually been integrated into water quality models that have followed the evolution of water quality problems. The HCIS model has been shown to be amenable to further improvements by eliminating some of the shortcomings associated with the aggregated dead zone, advection dispersion equation, and cell-in-series models (Ghosh, 2001; Ghosh et al.
Research Questions
4 This research aims to study the pollutant transport characteristics of the Umhlangane River using the Hydrologic Engineering Center River Analysis System (HEC-RAS) model and the Hybrid Cells in Series (HCIS) model.
Research Objectives
Overview of the Chapters
Background on Water Pollutants
Self-Purification
Effect of Pollutants on a water body – dissolved oxygen
The concentration of DO will increase until it reaches atmospheric equilibrium when the oxygen demand is less than the venting rate (Brown, 1995). When the oxygen demand is greater than the reaeration rate, the concentration of DO will decrease.
Development of Water Quality Models
The water surface quality models have gone through three major development phases (Wang et al., 2013). According to Post (1975), three-dimensional models were developed and sediments became an important element to be taken into account in the interaction processes of these models (Bai et al., 2012).
Advection Dispersion Equation
Problems with ADE and Alternative Models
In this model, the stretch length is assumed to be represented by a number of thoroughly mixed cells with equal residence times. 14 from a given cell is equal to the influence of the other cell, and the time is calculated from the injection of the solute into the first cell.
Common used water quality models
- Soil Water and Analysis Tools
- Water Quality Analysis Simulation Program
- MIKE 11
- QUALs
- Hybrid Cells In Series
- The HEC-RAS model
Water Quality Analysis Simulation Program (WASP) is a surface water quality model developed by the US Environmental Protection Agency for water quality modeling (Yang et al., 2007; Geza et al., 2009). It can be used to analyze a number of water quality problems in diverse water bodies such as ponds, reservoirs, lakes, streams, rivers, coastal waters and estuaries.
Difficulties within Water Quality Models
18 Water quality modeling can be very complex and difficult to perform especially with the increasing expectation of predicting water quality indicators with a high level of accuracy (Jorgensen et al., 1986). Therefore, it also pointed out that each model has its own assumptions and shortcomings (Ambrose et al., 2009).
Summary
Looking at it from another side, additional model complexity is expected to increase the precision of model results, but this has been shown to be unfounded in various studies (Gardner et al., 1980; Van der Perk, 1997; Lees et al., 2000 Young et al., 1996). In selecting an appropriate model for the study area, it is important to carefully analyze the data available for the study area so that the appropriate model can be selected to meet its input parameters (Young et al., 1996).
Introduction
Hydrologic Engineering Centers River Analysis System
Cn+1= concentration of a constituent at current time step (g/m3), Cup*= fastest concentration of a constituent at upstream (g/m3), Cn = concentration of a constituent at previous time step (g/m3), Cup* = fastest derivative of a constituent at upstream (g/m4), Dup= upstream surface diffusion coefficient (m2/s). The schematic diagrams showing an explanation of how geometric data is captured are shown in Figures 3.4 to 3.9; the parameters involved and calibration rates and constants were chosen and boundary condition requirements are shown in Figures 4.10 to 4.18.
Hybrid Cells In Series
- Conceptualisation of the HCIS Model
- Convolution technique for spatial variation of pollutants
- Estimation of the HCIS model parameters
- Reaeration Rate and Dispersion Coefficient
Where DO is the limiting solute concentration deficit Then the plug flow zone response for the given BOD becomes 𝐶𝐵(𝑥, 𝑡) = 𝐶𝑅𝑈 (𝑡 − = SDO – D. Similarly for the second mixed zone thoroughly, the effluent of the first mixed zone is the inlet to the second mixed zone and using the mass balance.
29 Therefore, the concentration of dissolved oxygen in the effluent is at the end of the second mixed zone. In rivers, the estimation of the longitudinal dispersion coefficient is influenced by several variables. 31 Table 3.1 shows some empirical equations developed by various researchers to predict the dispersion coefficient.
The challenge with existing dispersion equation models concerns their dispersion coefficient prediction accuracy. Therefore, Etemad-Sahidi and Taghipour (2012) formulated a new dispersion equation using the latest M5 algorithm.
HECRAS - Model Parameters
Geometric data
Channel characteristics
Summary
Introduction
Study Area
Data Sampling points
The data was collected and analyzed by Ethekweni Water Services technicians in their own Chemical and Microbiology laboratory (T0372) based in Pinetown. The locations of the sampling points where water quality data were collected are shown in Figure 4.1 and their individual coordinates are tabulated in Table 4.1. In addition, the average flow velocity, depth and width of this river were found to be 0.152 m/s, 1.5 m and 7.8 m respectively.
A summary of water quality parameters such as BOD, COD and DO observed over a period of twelve months is shown in Figures 4.2, 4.3 and 4.4. The only set of dissolved oxygen was recorded at the measuring station (MST) shown in Figure 4.1, and this is considered an important indicator in water quality assessment. The observed sets of collected data are discrete and sparse, which makes it difficult to perform good water quality analyses.
Despite these challenges, an effort must be made to come up with a reasonable analysis that will assist decision makers in the management of the Umhlangane River and help prevent further unnecessary deterioration of the water quality of this particular river. Other authors such as Rene & Saidutta (2008) and Marais & Ekama (1976) disagree and say that the type of pollutant source and the type of total pollutants play a significant role in the BOD to COD behavior.
Reaeration Rate and Dispersion Coefficient
HEC-RAS – Calibration and Boundary conditions
Calibration rates and constants
Boundary conditions
HCIS - Model Parameters
44 concentration of the BOD and COD are entered as the average values of the data obtained from the Ethekweni Water Services for each reach.
Root Mean Square Error
Summary
Introduction
HCIS Initial Response
47 all other three ranges except R12.9 to R 12.8, where it is reached in less than one hundred minutes. Similarly, COD concentrations become constant after four hundred minutes for the first range (R15 to R14) and for the other three in less than two hundred minutes.
HCIS Simulation Results – COD
HEC-RAS Simulation Results – BOD and COD
HEC-RAS and HCIS Simulation Results – DO
Root Mean Square Error
For parameter profiles, the errors appear reasonable and do not change much with the varying observed data. Figures 5.20 to 5.21 below show the maximum BOD and COD concentrations from the observed data per station at the end of each stretch along the Umhlangane River. 54 Figures 5.20 and 5.21 from the two graphs above show that the concentration of pollutants after the third station increases rather than decreases as it becomes diluted.
This is due to the wastewater treatment works built at the third station, which are intended to reduce water degradation. The simulation data plotted in the graphs above do not contain any data for the HCIS at station R15 and R12.9 because the actual recorded data at these stations are used as the input data to generate output at R14 and R12.8 respectively.
Summary
The performance of the HCIS and HEC-RAS models was evaluated by comparing the water quality simulation generated by both models with the observed data. The simulation results were then assessed with a view to the possibility of improving water quality of Umhlangane River. The HEC-RAS model was used as one of the models in this study because of the good water quality analysis capabilities it has shown over the years.
The performance of HCIS was also evaluated as one of the emerging models, to be further improved to meet modern water quality challenges. The new modified HCIS model was successfully tested and investigated by conducting a water quality analysis of the Umhlangane River. Analysis of the simulated water quality results generated by this model yielded some promising results when compared to actual recorded data (as described in chapter five).
The disadvantage of this model is that the BOD and COD inputs of the pollutants are assumed to be of constant mean values. 56 The results of the water quality parameters simulated by the HECRAS model were of good quality in terms of the root mean square error when compared to the actual recorded data.
The approximation to normality of the concentration distribution of a solute in a solvent flowing along a straight pipe. Sensitivity analysis and water quality modeling of a tidal river using a modified street container–. Phelps equation with HEC-RAS calculated hydraulic properties. An overview of currently available in-stream water quality models and their applicability for simulating dissolved oxygen in lowland rivers.
Modification of the Streeter-Phelps system to account for the response between dissolved oxygen concentration and the rate of oxidation of organic matter. Deoxygenation and reaeration Hybrid mixed cells coupled to pollutant transport model to assess the water quality status of a river. River water quality modeling for river basin and water resource management with a focus on the Saale River.
Organic and inorganic pollutants in the Bahlui River in the built-up area of the city of Iasi. 2007).Water Quality Modeling of Yamuna River Using QUAL2E-UNCAS. An efficient numerical solution of transient storage equations for solute transport in small streams. The influence of model structure on the accuracy and uncertainty of water quality model results.
An Imprecise Stochastic Constrained Programming Model for Water Quality Management in Binhai New Area of Tianjin, China.
