modeling of water quality

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DOKUZ EYLUL UNIVERSITY

GRADUATE SCHOOL OF NATURAL AND APPLIED

SCIENCES

MODELING OF WATER QUALITY IN A

DRINKING WATER BASIN

by

Sndz UTKU


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MODELING OF WATER QUALITY IN A

DRINKING WATER BASIN

A Thesis Submitted to the

Graduate School of Natural and Applied Sciences of Dokuz Eyll University

In Partial Fulfillment of the Requirements for the Degree of Master of Science

in

Environmental Engineering, Applied Environment Technology Program

by

Sndz UTKU


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M.Sc THESIS EXAMINATION RESULT FORM

We certify that we have read this thesis and MODELING OF WATER QUALITY

IN A DRINKING WATER BASIN completed by Sndz UTKUunder

supervision of Prof. Dr. Necdet ALPASLAN and that in our opinion it is fullyadequate, in scope and in quality, as a thesis for the degree of Master of Science.

Supervisor

(Jury Member) (Jury Member)

Approved by the


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ACKNOWLEDGEMENTS

The author is grateful to Prof. Dr. Necdet ALPASLAN, the advisor of the M. Sc.

Thesis for his support; to Research Assistant Hlya BOYACIOLU for her kind

contributions and efforts on this study.

I thank to my family, they were always with me. Finally, thanks to my husband

Semih for his patience and understandings in support of pursue of the M.Sc. degree.

Sndz Bayraktar UTKU


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MODELING OF WATER QUALITY IN ADRINKING WATER BASIN

ABSTRACT

Most of drinking water basins on the World are under the risk of pollution

due to anthropological activities. Therefore, development of an efficient basin

management plan is essential. The identification of water quality parameters in space

and time dimensions is required for a good management plan. For this purpose,

computer based simulation models have improved and applied on a wide area.

QUAL2K Water Quality Model (one of the mostly used surface water quality model)

is examined and applied in this presented study. QUAL2K is a computer program

packet which is used to estimate the levels of pollution and the pollution sources by

modeling of water quality. In this research, firstly model is introduced and inputs,

characteristics of the model is explained. Then, a part of Tahtali Basin, which is one

of the most important drinking sources of Izmir, is modeled. There are many point

and non-point (diffuse) pollution sources on the studied basin. Pollution sources on

the modeled tributary are designated and the scenarios are formed according to these

pollution sources. The results obtained from these probable scenarios, reveal the

level of the magnitude of the pollution on the tributary, so that, it may be used as an

important tool for decision-makers.

Keywords : Basin Management, Surface Water Quality Modeling, Qual2K,

Pollution Sources (point and diffuse sources)


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ME SUYU HAVZASINDA SU KALTESNNMODELLENMES

ZET

Dnyadaki birok ime suyu havzas antropolojik aktivitelerden dolay

kirlenme riski tamaktadr. Bu amala her havza iin etkili bir havza ynetim plan

gelitirmek zorunlu hale gelmitir. yi bir ynetim plan iin, havzadaki su kalite

parametrelerinin zamana ve yere gre tanmlanmas gerekmektedir. Bu yzden

bilgisayar destekli simulasyon modelleri gelitirilmive genibir alanda uygulamaya

balanmtr. Daha ok yzeysel sularn modellenmesinde kullanlan QUAL2K Su

Kalitesi Modeli sunulan alma kapsamnda incelenmi ve uygulanmtr.

QUAL2K, su kalitesini modelleyerek oluan kirliliin boyutlarn ve kirletici

kaynaklar belirlemede kullanlan bir bilgisayar program paketidir. Bu tezde

ncelikle model tantlm, modelin alma prensibi, girdileri ve zellikleri

aklanmtr. Daha sonra zmirin en nemli ime suyu kaynaklarndan biri olan

Tahtal Havzasnn bir paras zerinde modelleme almas yaplmtr. Bu

havzann seilmesinin nedeni, havzada ok sayda noktasal ve noktasal olmayan

kirletici kaynan bulunmasdr. Bu amala, modellenen koldaki kirletici kaynaklar

belirlenmi ve bu kirletici kaynaklara gre senaryolar oluturulmutur. Bu olas

senaryolardan elde edilen sonular, havzann bu kolu zerindeki kirlenmenin

boyutunu gstermektedir. Modelleme almalar bu zellii ile karar verici

mekanizmalarn daha ok ilgisini ekmektedir. Bu nedenle, sunulan alma ile,

havza ynetiminde karar vericiye nemli bir ara salanmolacaktr.

Anahtar Szckler : Havza Ynetimi, Yzeysel Su Kalite Modellemesi, Qual2K,

Kirletici Kaynaklar (Noktasal ve Noktasal olmayan kaynaklar).


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CONTENTS

Page

THESIS EXAMINATION RESULT FORM ........................................................... ii

ACKNOWLEDGEMENTS ................................................................................... iii

ABSTRACT............................................................................................................iv

ZET .. ...............................................................................................................v

CONTENTS.. ......................................................................................................vi

LIST OF TABLES....................................................................................................x

LIST OF FIGURES ............................................................................................... xii

CHAPTER ONE INTRODUCTION ..................................................................1

1.1 INTRODUCTION .......................................................................................1

CHAPTER TWO - MODELING ...........................................................................4

2.1 GENERAL MODEL DEFINITION.............................................................42.2 MODEL CLASSIFICATION.......................................................................5

2.3 WATER QUALITY MODELS....................................................................8

2.4 MODEL CALIBRATION AND VERIFICATION.....................................10

CHAPTER THREE - QUAL2K MODEL .........................................................12

3.1 OVERWIEV OF QUAL2K........................................................................12

3.2 BACKGROUND OF QUAL2K .................................................................13

3.3 QUAL2K APPLICATION.........................................................................14

3.4 WORKSHEETS USED IN QUAL2K........................................................15


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3.4.5 RATES WORKSHEET.....................................................................17

3.4.6 LIGHT AND HEAT WORKSHEET.............................. ...................183.4.7 POINT SOURCE WORKSHEET .....................................................18

3.4.8 DIFFUSE SOURCE WORKSHEET .................................................19

3.4.9 DATA WORKSHEET ......................................................................19

3.4.10 OUTPUT WORKSHEET................................................................20

3.4.11 SPATIAL CHARTS........................................................................20

3.4.12 DIEL CHARTS...............................................................................21

3.5 SEGMENTATION AND HYDRAULICS IN THE MODEL .....................22

3.5.1 FLOW BALANCE............................................................................22

3.5.2 HYDROULIC CHARACTERISTICS...............................................23

3.5.3 TRAVEL TIME................................................................................23

3.5.4 LONGITUDINAL DISPERSION .....................................................233.6 TEMPERATURE MODEL........................................................................24

3.6.1 SURFACE HEAT FLUX ..................................................................24

3.6.2 SEDIMENT-WATER HEAT FLUX.............................. ...................25

3.7 CONSTITUENTS OF THE MODEL.........................................................26

3.7.1 CONSTITUENTS AND GENERAL MASS BALANCE ..................26

3.7.2 REACTION FUNDAMENTALS......................................................28

3.7.3 CONSTITUENT REACTIONS.........................................................30

3.7.4 SOD / NUTRIENT FLUX MODEL..................................................32

CHAPTER FOUR - DEFINITION OF THE STUDY AREA .............................35

4.1 TAHTALI BASIN .....................................................................................35

4.2 EXISTING CONDITION ON THE PROTECTION ZONES .....................37

4.3 POLLUTANT SOURCES OF THE TAHTALI BASIN .......... ...................39

4.4 MENDERES SEHITOGLU CREEK....................................... ...................43


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CHAPTER FIVE - WATER QUALITY VARIABLES FOR MODELING ......49

5.1 GENERAL ................................................................................................49

5.2 GRAPHICS OF THE WATER QAULITY VARIABLES..........................49

5.3 STATISTICAL ANALYSIS OF THE WATER QUALITY VARIABLES.50

5.4 EVALUATION OF STATISTICAL ANALYSIS ................... ...................52

CHAPTER SIX - APPLICATION OF THE MODEL TOTHE STUDY

AREA .......................................................................................54

6.1 GENERAL ................................................................................................54

6.2 HEADWATER DATA ..............................................................................55

6.3 REACH DATA..........................................................................................59

6.4 METEOROLOGY AND SHADING DATA..............................................616.5 RATES, LIGHT AND HEAT DATA.........................................................62

6.6 POINT SOURCES DATA .........................................................................64

6.7 DIFFUSE SOURCES DATA.....................................................................81

6.8 TEMPERATURE DATA...........................................................................83

6.9 WATER QUALITY DATA .......................................................................84

CHAPTER SEVEN - EVALUATION OF THE RESULTS ...............................85

7.1 GENERAL ................................................................................................85

7.2 EVALUTION OF THE GRAPHS........................................... ...................86

CHAPTER EIGHT CONCLUSION...............................................................113

REFERENCES ...................................................................................................114

APPENDICES.....................................................................................................116


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APPENDIX C : WATER QUALITY CLASSIFICATION FOR SURFACE

WATERS ...................................................................................125APPENDIX D: DEW POINT TEMPERATURE CALCULATION .....................128

APPENDIX E: DOMESTIC WASTEWATER CHARACTERISTICS .................131

APPENDIX F: LAND USE DISTRIBUTION OF TAHTALI DAM BASIN ........133


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CHAPTER ONE

INTRODUCTION

World's water supply is a precious commodity necessary for human survival.

Water is fundamental to all life forms, affecting all ecosystems and the various uses

to which it is put. Water resources in the world can be grouped generally as surface

water and groundwater and they must be managed to ensure they can be exploited

safely and economically, while preserving their natural and recreational values.

Quality of the water is important as much as quantity of water resources. Agriculture,

industry, and rapidly expanding populations affect the water quality and water

demand. These limited water resources may have high risk as qualitatively because

of despoliation of wastes and land runoff.

Surface water quality is important in many aspects. Water is used for

different purposes (irrigation, drinking water, water use,etc.). Little amount of

worlds water is used as drinking water supply. Most of the drinking water is

supplied from groundwater resources because of its better quality. However, due to

densely population, especially for big cities, the amount of groundwater sources have

become unsatisfactory to supply all demand; therefore, surface water have been used

as another drinking water source. The main problem about the use of surface water

for drinking water purposes is its quality. Because, it is mainly subjected to

contamination and quality deterioration. The primary causes of deterioration of

surface water quality are municipal and domestic wastewater, industrial and

agricultural wastes, and solid and semisolid refuse. Therefore all these inputs shouldbe eliminated and controlled in order to protect surface water resource. This is

achieved by a basin management approach. Thus, proper basin management plans

should be prepared in order to solve water quality problems in the basins. Many tools

can be used for planning studies One of these tools is mathematical modeling In


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is used as model) in identification of processes that underlie water quality problems

in a basin.

In this research, a drinking water quality basin (namely, Tahtali) of Izmir is

considered. The basin provides about 30% drinking water demand of the city. Tahtali

Dam was constructed to collect, store and abstract the water. Various tributaries and

creeks in the basin feed the reservoir of the dam. So, water quality in the reservoir is

affected from the water quality in the feeding creeks and tributaries. It is obvious that

some processes take place in the reservoir, such as sedimentation, eutrofication,

some biological reactions on the water column as well as on the bottom. These

processes impair or improve the quality of water in the reservoir. However, the

quality in the feeding creeks and tributaries is the main factor affecting the water

quality in the reservoir. Therefore the general water quality in the reservoir can beattributed to water quality in creeks as well as the reactions taken place in the

reservoir.

This thesis focuses on the water quality issues in one of the important

tributary (Menderes Sehitoglu Creek) in the basin. In this framework, the prevailing

and collected data from the Izmir Sewage and Water Authority (IZSU) and the

Regional Directorate of State Hydraulic Works (DSI) are processed and evaluated.

In the research, firstly, statistical analysis of the related water quality data of

the creek is examined. The existing condition of the variables in the water is

evaluated by using classical statistical computation. In the second step of the research

QUAL2K model is used for making a good planning for the studied basin. The

model is downloaded from Environmental Protection Agency (EPA) and run by

using existing data. Different scenarios are developed during the model application.

These scenarios help to decision making process for different probable cases and also


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In the second chapter of the thesis, general information about mathematical

modeling and models are explained. In the third chapter, the information aboutQual2k model is produced; model parameters and other tools were presented. Fourth

chapter is related with the basin. The information about the Tahtali Basin and studied

tributaries Menderes Sehitoglu Creek are given. At fifth chapter, existing condition

of the variables are evaluated by using statistical analysis. Sixth chapter is concerned

about Qual2k model application. The input data and default values of the model are

prepared and loaded. Then model is run by using those inputs. Different scenarios are

accepted. Each scenario is examined by using the model. Seventh chapter

summarizes the basic results derived by model application. Outputs of the model are

received in graphical. At eighth chapter, the research is evaluated. Conclusion is

withdrawn from the conducted studies.


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CHAPTER TWO

MODELLING

2.1General Model Definition

Models are simple or complicated mathematical expressions that are used to

simulate environmental processes. In other words, model can be defined as the

process of application of fundamental knowledge or experience to simulate or

describe the performance of a real system to achieve certain goals. Models can be

cost-effective and efficient tools whenever it is more feasible to work with a

substitute than with the real, often complex systems. Modeling has long been an

integral component in organizing, synthesizing, and rationalizing observations of and

measurements from real systems and in understanding their causes and effects(Khandan, 2002).

Today, environmental studies have to be multidisciplinary, dealing with a wide

range of pollutants undergoing complex biotic and abiotic processes in the soil,

surface water, groundwater, ocean water, and atmospheric compartments of the

ecosphere. In addition, environmental studies also encompass equally diverse

engineered reactors and processes that interact with the natural environment through

several pathways. Consequently, modeling of large-scale environmental systems is

often a complex and challenging task (Khandan, 2002).

Recently, some facilities applied by human have been affected to natural

environmental processes. The ability to predict the ecological impacts of these

activities is now a fundamental requirement for environmental planners and

managers. The use of computer-based ecological and water quality models is widely

accepted for this purpose In addition it can be said that one of the main objectives


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will need to be gathered on site to make any model operational. An economic

analogue might be the use of input-output analysis of a regional economy

Mathematical models can be used to predict changes in ambient water quality due

to changes in discharges of wastewater. The models are typically used to establish

priorities for reduction of existing wastewater discharges or to predict the impacts of

a proposed new discharge. Although a range of parameters may be of interest, a

modeling exercise typically focuses on a few, such as dissolved oxygen, coliform

bacteria, or nutrients. Hydraulic data are also important for modeling studies.

Dynamic models need time-series data on flows, temperatures, and other parameters.

In addition to hydraulic data, models require base-case concentrations of the water

quality parameters of interest (dissolved oxygen, mercury, and so on). These are

required both to calibrate the models to existing conditions and to provide a baseagainst which to assess the effects of management alternatives. The models also need

discharges or loads of the pollutants under consideration from the sources (e.g.,

industrial plants) being studied. The types and amounts of data needed for a given

application are specific to the management question at hand [World Bank Group

(WBG), 1998].

Predicting the water quality impacts of a single discharge can often be done

quickly and sufficiently accurately with a simple model. Regional water quality

planning usually requires a model with a broader geographic scale, more data, and a

more complex model structure.( WBG,1998).

2.2 Model Classification

Water quality models are usually classified according to model complexity, type

of receiving water and the water quality parameters (dissolved oxygen nutrients


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and has been studied more intensively than have other parameters. Basic nutrient

indicators such as ammonia, nitrate, and phosphate concentrations can also bepredicted reasonably accurately, at least for simpler water bodies such as rivers and

moderate-size lakes. Predicting algae concentrations accurately is somewhat more

difficult but is commonly done in the United States and Europe, where

eutrophication has become a concern in the past two decades. Toxic organic

compounds and heavy metals are much more problematic. Although some of the

models reviewed below do include these materials, their behavior in the environment

is still an area of active research. These classification criteria for water quality

models take place in Table 2.1.

Table 2.1 Criteria for classification of water quality models (WBG,1998).

Criterion Comment

Single-plant or regional focusSimpler models can usually be used for single-plantmarginal effects. More complex models areneeded for regional analyses.

Static or dynamic Static (constant) or time-varying outputs.

Stochastic or deterministicStochastic models present outputs as probabilitydistributions; deterministic modelsare point-estimates.

Type of receiving water(river, lake, or estuary)

Small lakes and rivers are usually easier to model.Large lakes, estuaries, and largeriver systems are more complex.

Water quality parameters

Dissolved oxygenUsually decreases as discharge increases. Used as awater quality indicator in most water qualitymodels.

Biochemical oxygen demand(BOD)

A measure of oxygen-reducing potential forwaterborne discharges. Used in mostwater quality models.

TemperatureOften increased by discharges, especially fromelectric power plants. Relatively easyto model


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Coliform bacteriaAn indicator of contamination from sewage andanimal waste

NitratesA nutrient for algal growth and a health hazard atvery high concentrations in drinkingwater. Predicted by moderately complex models.

Phosphates Nutrient for algal growth. Predicted by moderatelycomplex models.

Toxic organic compoundsA wide variety of organic (carbon-based)compounds can affect aquatic life and may

be directly hazardous to humans. Usually verydifficult to model.

Heavy metalsSubstances containing lead, mercury, cadmium, andother metals can cause bothecological and human health problems. Difficult tomodel in detail.

Models can cover only a limited number of pollutants. In selecting parameters for

the model, care should be taken to choose pollutants that are a concern in them and

are also representative of the broader set of substances which cannot all be modeled

in detail. The more complex the model is, the more difficult and expensive will be its

application to a given situation. Model complexity is a function of four factors

(WBG, 1998);

1. The number and type of water quality indicators: In general, the more indicators

that are included, the more complex the model will be. In addition, some indicators

are more complicated to predict than others

2. The level of spatial detail: As the number of pollution sources and water quality

monitoring points increase, so do the data required and the size of the model.

3. The level of temporal detail: It is much easier to predict long-term static averages

than shortterm dynamic changes in water quality. Point estimates of water quality


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4. The complexity of the water body under analysis: Small lakes that mix

completely are less complex than moderate-size rivers, which areless complex thanlarge rivers, which are less complex than large lakes, estuaries, and coastal zones.

2.3 Water Quality Models

Water quality modeling as a planning and management tool requires the package

to be as comprehensive as possible so as to provide necessary decision support

criteria for users. Many software models are developed for water quality. These

models predict the response of the receiving water body to a set of pollutant loadings,

by simulating the processes that occur within water bodies. For example, these

models can predict the effects of hydrodynamic factors, such as flow, and temporal

factors, such as the time it takes for certain pollutants to break down in the system.Receiving models also account for the location of the pollutant sources and for non-

conservative pollutants. There are far too many models in use. However, we can

highlight a few specific models that have been used.

BASINS, Better Assessment Science Integrating Point and Nonpoint Sources

(BASINS) is an integrated model that includes both receiving water and watershed-

scale loading models. It is a collection of existing models, packaged together with a

graphical GIS-based user interface. It is used for modeling nutrients, sediment,

bacteria and toxics (Frey et al. 2002).

HSPF, The Hydrological Simulation Program FORTRAN (HSPF) model is awatershed-scale integrated model that allows you to calculate surface runoff and

subsurface discharge of pollutants. It also models receiving water quality. HSPF is a

dynamic model and has been applied extensively. It is used for well mixed streams,


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source control, land use changes, and best management practices. It is used for DO,

bacteria, pesticides, algae, total P, total N, TOC, TSS, acid mine, drainage pollutants

(Frey et al. 2002).

WASP6, Water Quality Analysis Simulation Program (WASP6) is a receiving

water model that is used to assess the fate and transport of both conventional and

toxic pollutants. It predicts concentrations of water quality parameters over time. It is

used for river, streams, lakes, reservoirs, estuaries, and coastal waters. The prediction

of the fate and transport of organic chemicals (PCB, PAH, TCE, Dioxin), and metals

(simple speciation) (Frey et al. 2002).

HEC-5Q, Developed primarily for analyzing water flows and water quality in

reservoirs and associated downstream river reaches. It can perform detailed

simulations of reservoir operations, such as regulating outflows through gates and

turbines, and vertical temperature gradients in reservoirs (WBG, 1998).

Finally, QUAL2E, The Enhanced Stream Water Quality Model (QUAL2E) is a

receiving water model that can simulate multiple parameters in a branching stream

system. It is used for streams, rivers, lakes, reservoirs, and estuaries. Pollutants,

which are used in the model, dissolved oxygen, BOD, temperature, chlorophyll a,

ammonia, nitrite, nitrate, organic N, organic and dissolved phosphorus, coliforms,

and more (Frey et al. 2002). Recently, QUAL2K (which is used in this research) is

has been released as a modernized version of the QUAL2E (or Q2E) model (Brown

and Barnwell 1987). Both QUAL2K and QUAL2E model represent the field data

quite well except for some parameters of QUAL2E. In BOD, DO, and total nitrogen,

there are significant discrepancies between the results of two models, where

QUAL2K displayed better agreement with the field measurements than QUAL2E

due to QUAL2Ks ability to simulate the conversion of algal death to BOD fixed


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the QUAL2E model, such that the model cannot simulate the large river system with

high accuracy. The major enhancements of the QUAL2K model include the

expansion of computational structure and the addition of new constituent

interactions, such as algal BOD, denitrification, and DO change caused by fixed

plant. Most of the model equations included in QUAL2K are same as in QUAL2E,

except for DO, BOD, and nitrate (Park et al. 2001). As stated before, this QUAL2K

model is used in the presented research for simulating the Tahtali case. Because,

QUAL2K include more detail and its results can be more realistic.

A mass balance equation compares the mass of a pollutant that enters a defined

area with the mass leaving the area. But keep in mind that there are often several

ways for a pollutant to enter or exit an area. For example, chemical reactions may

transform a pollutant into something else, or a pollutant may adsorb to sediment and

settle out of the water column. Mass balance equations must therefore account for

not just the initial input of a pollutant to a water segment and the transport of the

pollutant through the segment, but also reactions and changes in storage within the

segment. The complexity of a receiving water model depends on how it incorporates

pollutant inputs, reactions, and transport into the model. For example, the simplest

steady-state models use constant inputs that do not vary over time. More complex

dynamic models allow inputs to vary day-by-day or hour-by-hour and may consider

complex reactions among different pollutants.

2.4 Model Calibration and Verification

Calibration and verification should be used to gain confidence that the model is a

reasonably accurate representation of reality. Calibration involves fine-tuning the

model by tweaking input data in appropriate ways so that the model results better

predict reality This process involves entering data into the model running the


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set of data. Then, the data that was set aside would be entered into the calibrated

model, and the model would be run again to see how well the calibrated model

predicts instream flows and concentrations using this second set of data (Frey et al.

2002).

Models can be calibrated and verified using historical data or recent data. Its

more important to have enough of the right kind of data over a particular time period;

its less important whether this time period occurred a decade ago or just last year.

However, if substantial changes have occurred in the watershed over the past decade,

using old data to calibrate the model would cause problems (Frey et al. 2002). On the

other hand, calibrating and verifying a model with existing historical data can save

time and money, since no new monitoring is required. At the same time, lack of data

can create three problems (WBG, 1998); first, a model cannot be calibrated and

tested until a monitoring system has been designed and operated for a considerable

length of time. Second, water sample collection and analysis may be considerably

more expensive than the modeling effort that it is designed to support. Finally, design

of a monitoring system may fall prey to the same types of problems that can affect

water quality modeling, including a lack of clear connections to management

objectives and a tendency to excessive complexity.

Models are only an abstraction from the reality of a situation, and the improper

use or misinterpretation of outputs from a model can lead to imprecise or incorrect

results. Any conclusions reached on the basis of a model should therefore always be

checked for realism and common sense.


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CHAPTER THREE

QUAL2K MODEL

3.1 Overview of QUAL2K

Modifications were made in the computer code to overcome some limitations and

the modified version was named as QUAL2K, which stands for 2000 Year Version

of USEPAs QUAL2E (Park et al. 2001). The major enhancements of the QUAL2K

model include the expansion of computational structure and the addition of new

constituent interactions, such as algal BOD, denitrification, and DO change caused

by fixed plant. Installation is required for many water-quality models. This is not the

case for QUAL2K because the model is packaged as an Excel Workbook. The

program is written in Excels macro language: Visual Basic for Applications or

VBA. The Excel Workbooks worksheets and charts are used to enter data and

display results (Chapra et al.2003).

QUAL2K (or Q2K) is a river and stream water quality model that is intended to

represent a modernized version of the QUAL2E (or Q2E) model (Brown and et al.

1987). Q2K is similar to Q2E in the following respects; one dimensional, the channel

is well-mixed vertically and laterally. Steady state hydraulics, non-uniform, steady

flow is simulated. Diurnal heat budget, the heat budget and temperature are

simulated as a function of meteorology on a diurnal time scale. Diurnal water-quality

kinetics, all water quality variables are simulated on a diurnal time scale. Heat and

mass inputs, point and non-point loads and abstractions are simulated.

The QUAL2K framework includes the following new elements; Software

Environment and Interface, Q2K is implemented within the Microsoft Windows

environment It is programmed in the Windows macro language: Visual Basic for


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speciation, Q2K uses two forms of carbonaceous BOD to represent organic carbon.

These forms are a slowly oxidizing form (slow CBOD) and a rapidly oxidizing form

(fast CBOD). In addition, non-living particulate organic matter (detritus) is

simulated. This detrital material is composed of particulate carbon, nitrogen and

phosphorus in a fixed stoichiometry. Anoxia, Q2K accommodates anoxia by

reducing oxidation reactions to zero at low oxygen levels. In addition, denitrification

is modeled as a first-order reaction that becomes pronounced at low oxygen

concentrations. Sediment-water interactions, sediment-water fluxes of dissolved

oxygen and nutrients are simulated internally rather than being prescribed. That is,

oxygen (SOD) and nutrient fluxes are simulated as a function of settling particulate

organic matter, reactions within the sediments, and the concentrations of soluble

forms in the overlying waters. Bottom algae, the model explicitly simulates attached

bottom algae. Light extinction, light extinction is calculated as a function of algae,

detritus and inorganic solids. pH, both alkalinity and total inorganic carbon are

simulated. The rivers pH is then simulated based on these two quantities. Pathogens,

generic pathogen are simulated. Pathogen removal is determined as a function of

temperature, light, and settling (Chapra et al.2003).

3.2 Background of QUAL2K

QUAL2E is the result of a historical development of O, N and P models (Rauch et

al., 1998) which were given step-by step extensions and increasing complexity. The

starting point was the pioneer Streeter-Phelps model (Streeter and Phelps, 1925)

describing the increase and following decrease of the oxygen deficit downstream of a

source of organic material. It was later extended by nitrogen processes that included

especially nitrification, the resulting model is called QUAL1 (Orlob, 1982). Finally,

the phosphorus cycling and algae were added in creating the QUAL2 model family

(Brown et al 1987) Several versions of QUAL2 are available depending on the


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3.3 QUAL2K Application

QUAL2K formulation derives directly from the U.S. regulatory framework.

(Shanahan et al. 1998). More specifically, QUAL2K is very well suited for waste

load allocation studies and other planning activities (Brown and et al. 1987).

Wasteload allocations are performed for conditions of constant low flow (U.S.

regulations: seven-consecutive-day low flow with a probability occurring once in ten

years, (Shanahan et al., 1998) and maximum permitted effluent discharge rate.

QUAL2K is intended specifically for the steady-streamflow, steady-effluent-

discharge conditions specified in the water quality regulations for wasteload

allocation. As a result, QUAL2K has been widely used by consultants and regulatory

agencies and is considered as the standard for water quality models (Chapra, 1997,

Shanahan et al., 1998).

Dissolved oxygen is usually the looked-at state variable, especially during waste

allocation studies. However, the model can be used for non-point source studies,

where DO and CBOD do not have to be simulated jointly with the nitrogen and

phosphorus cycles. Diurnal responses of temperature and DO can also be simulated

QUAL2K.

Although, the model is very well suited for its intentional use, it does not work

well for usage beyond its explicit limitations. The model computes mass transport

and diffusion in one dimension and therefore is suited for streams that are well mixed

vertically and laterally. The model is unsuitable for rivers that experience temporal

variations in streamflow or where the major discharges fluctuate significantly over a

diurnal or shorter time period. More significant are the limitations of the model when

examining the contribution of nonpoint sources of pollutants to river water quality

degradation Indeed nonpoint source loads are often driven by rainfall events and

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3.4 Worksheets Used in QUAL2K

3.4.1

QUAL2K Worksheet

The QUAL2K Worksheet is used to enter general information regarding a

particular model application. These information are river name, file name, file

directory, month, day, year, time zone, daylight savings time, calculation step, final

time, program determined calc step (output), time of last calculation (output), time of

sunrise, time of solar noon, time of sunset, photoperiod.

3.4.2

Headwater Worksheet

This worksheet is used to enter flow and concentration for the systems

boundaries. These are flow as m3/s, headwater water quality, and downstream

boundary water quality.

3.4.3

Reach Worksheet

This worksheet is used to enter information related to the rivers headwater(Reach Number 0) and reaches. There is some optional information. These are reach

label and downstream end of reach label. Some information is computed

automatically as output. These are reach numbers, reach length, downstream latitude

and longitude. Some data are needed in this sheet. They are downstream location,

upstream and downstream elevation, downstream latitude and longitude (degrees,

minutes, and seconds) and hydraulic model.

Hydraulic model includes two options for computing velocity and depth based on

flow: rating curves or the Manning formula It is important to pick one of the options

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For Rating Curves:

Velocity coefficient. (a),

Depth coefficient. (),

Velocity exponent. (b),

Depth exponent.

For Manning Formula:

Bottom width,B0 (m), Side slope,

Channel slope,

Manning n, dimensionless number that parameterizes channel roughness.

Values for weedless man-made canals range from 0.012 to 0.03 and for natural

channels from 0.025 to 0.2 a value of 0.04 is a good starting value for many natural

channels.

Other required data are prescribed dispersion, weir height, prescribed reaeration,

bottom algae coverage, bottom SOD coverage, prescribed SOD, prescribed CH4

(Methane) flux, prescribed NH4 (Ammonium) flux, prescribed inorganic phosphorus

flux. If there is any information about them, these data are entered.

3.4.4

Meteorology and Shading Worksheets

Five worksheets are used to enter meteorological and shading data. They are air

temperature worksheet, dew-point temperature worksheet, wind speed worksheet,

cloud cover worksheet and shade worksheet. Air temperature worksheet; this

worksheet is used to enter hourly air temperatures in degrees Celcius for each of the

systems reaches. Dew-Point temperature worksheet; this worksheet is used to enter

hourly dew point temperatures (degrees Celcius) for each of the systems reaches

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reaches.Shade worksheet; this worksheet is used to enter hourly shading for each of

the systems reaches.

3.4.5

Rates Worksheet

This worksheet is used to enter the models rate parameters. These parameters are

related with stoichiometry, inorganic suspended solids, oxygen, slow C, fast C,

organic N, ammonium, nitrate, organic P, floating plants (Phytoplankton), bottomalgae, pH, pathogens, detritus (POM). The model assumes a fixed stoichiometry of

plant and detrital matter. It should be noted that chlorophyll is the most variable of

these values with a range from about 0.5 to 2 mgA.Recommended values for these

parameters are listed below;

Recommended values for stoichiometry.

Carbon 40 mgC

Nitrogen 7.2 mgN

Phosphorus 1 mgP

Dry weight 100 mgD

Chlorophyll 1 mgA

There are some models and constant are used for oxygen.

Reaeration model. The reaeration is computed internally depending on the

rivers depth and velocity (Covar 1976)), OConnor-Dobbins formula, Churchill

formula.,Owens-Gibbs formula.

Temperature correction (reaeration). Suggested value: 1.024.

O2 for CBOD oxidation. Suggested value: 2.69 gO2/gC.

O2 f NH4 it ifi ti S t d l 4 57 O2/ C

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Oxygen inhibition nitrification model. Options are: Half-saturation,

Exponential, Second order

Oxygen inhibition nitrification parameter.

Oxygen enhancement denitrification model. Options are: Half-saturation,

Exponential, Second order.

Oxygen enhancement denitrification parameter.

3.4.6

Light and Heat Worksheet

This worksheet is used to enter information related the systems light and heat

parameters. These are photosynthetically available radiation(0.47), background light

extinction, linear chlorophyll light extinction( according to Riley (1956) 0.0088/m

gA/L), nonlinear chlorophyll light extinction (according to Riley (1956) 0.054/m

(gA/L)2/3)), inorganic suspended solids light extinction, detritus light extinction,

atmospheric attenuation model for solar (Bras or the Ryan-Stolzenbach models.),

atmospheric turbidity coefficient for Bras (2=clear, 5=smoggy, default=2),

atmospheric transmission coefficient for Ryan-Stolzenbach (0.70-0.91, default 0.8),

atmospheric longwave emissivity model (Brutsaert, Brunt or Koberg models), wind

speed function for evaporation and air convection/conduction (Brady-Graves-Geyer,

the Adams 1, or the Adams 2 models).

3.4.7

Point Sources Worksheet

This worksheet is used to enter information related the systems point sources.

This information is name of the source, location of the source, source inflows and

outflows, constituents (the temperature and the water quality concentrations). If there

is a point abstraction, a positive value for flow (m3/s) must be entered and values for

i fl h ld b l ft bl k If th i i t i fl l f fl ( 3/ ) t b

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3.4.8

Diffuse Sources Worksheet

This worksheet is used to enter information related the systems diffuse (i.e., non-

point) sources. This information is name of source, location of source, source inflows

and outflows. If there is a point abstraction, a positive value for flow (m3/s) must be

entered and values for inflow should be left blank. If there is a point inflow, a value

for flow (m3/s) must be entered.

3.4.9

Data Worksheets

Hydraulics Data Worksheet; this worksheet is used to enter data related to the

systems hydraulics. These data are distance (km), flow data (Q-data, m3/s), depth

data (H-data, m), velocity data (U-data,m/s), travel time-data. Temperature Data

Worksheet; this worksheet is used to enter temperature data. These are distance (km),

mean temperature-data (0C), minimum temperature-data (0C), and maximum

temperature-data (0C). Water Quality Data Worksheet; this worksheet is used to enter

mean daily values for water quality data. They are distance (km), constituents (other

concentrations and fluxes.) Bottom Algae, total nitrogen-data, total phosphorus-data,

total suspended solids-data, NH3 (unionized ammonia)-data, % saturation-data,SOD-data, sediment ammonium flux, sediment methane flux, sediment inorganic

phosphorus flux, ultimate carbonaceous BOD. This is the total of detritus, slow

CBOD, fast CBOD, and phytoplankton biomass expressed as oxygen equivalents.

This is the total of inorganic suspended solids, phytoplankton biomass and detritus

expresed as dry weight. Water Quality Data Min Worksheet; this worksheet is used

to enter minimum daily values for water quality data. Water Quality Data Max

Worksheet; this worksheet is used to enter maximum daily values for water quality

data. Diel Data Worksheet; this worksheet is used to enter diel data for a selected

reach This data is then plotted as points on the graphs of diel model output (Chapra

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3.4.10

Output Worksheets

These are a series of worksheets that present tables of numerical output generated

by Q2K. They are source summary, hydraulics summary, temperature output, water

quality output, water quality minimum, water quality maximum, sediment fluxes

(This worksheet summarizes the fluxes of oxygen and nutrients between the water

and the underlying sediment compartment for each model reach.), diel output

worksheet.

3.4.11

Spatial Charts

QUAL2K displays a series of charts that plot the model output and data versus

distance (km) along the river. Figure-3.1 shows an example of the plot for dissolved

oxygen. The black line is the simulated mean DO (as displayed on the WQ

Worksheet), whereas the dashed red lines are the minimum (WQ Min Worksheet)

and maximum (WQ Max Worksheet) values, respectively. The black squares are the

measured mean data points that were entered on the WQ Data Worksheet. The white

squares are the minimum (WQ Min Worksheet) and maximum (WQ Max

Worksheet) data points, respectively. The plot is labeled with the river name and thesimulation date. Notice that this plot also displays the oxygen saturation as a dashed

line. (see figure 3.1)

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The following series of variables are plotted; Hydraulics Plots: travel Time, flow,

velocity, depth, reaeration. Temperature and state-variable plots: temperature,

conductivity, ISS (Inorganic suspended solids), dissolved oxygen, detritus, slow

CBOD, fast CBOD, DON (Dissolved organic nitrogen), NH4 (Ammonia nitrogen),

NO3 (Nitrate nitrogen), DOP (Dissolved organic phosphorus), inorganic phosphorus,

phytoplankton, Bot Pl gD per m2 (Bottom algae in units of gD/m2), pathogen,

alkalinity, pH. Additional State-variable plots: Bot Pl mgA per m2 (Bottom algae in

units of mgA/m2), CBODu, NH3, TN and TP, TSS. Sediment-water plots: SOD,CH4 sed. flux, NH4 sed. flux, inorg P sed. flux.

3.4.12

Diel Charts

QUAL2K displays a series of charts that plot the model output and data versus

time of day (in hours) for temperature and the model state variables. Figure 3.2

shows an example of the diel plot for pH. The red line is the simulated pH (as

displayed on the Diel Worksheet). The black squares are the measured data points

that were entered on the Diel Data Worksheet. The plot is labeled with the river

name, the date and the name of the reach that is plotted.

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3.5 Segmentation and Hydraulics in the Model

The model presently simulates the main stem of a river as depicted in Figure-3.3

Tributaries are not modeled explicitly, but can be represented as point sources.

Figure 3.3 Segmentation scheme

3.5.1

Flow Balance

A steady-state flow balance is implemented for each model reach (Figure-3.4).

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Qi+1 outflow from reach i into reach i + 1 [m3/d], Qi1= inflow from the upstream

reach i 1 [m3/d], Qin,i is the total inflow into the reach from point and nonpoint

sources [m3/d], and Qab,iis the total outflow from the reach due to point and nonpoint

abstractions [m3/d]. The total inflow from sources and total outflow from abstraction

are computed in the model. The non-point sources and abstractions are modeled as

line sources. The nonpoint source or abstraction is demarcated by its starting and

ending kilometer points. Its flow is distributed to or from each reach in a length-

weighted fashion.

3.5.2

Hydraulic Characteristics

Once the outflow for each reach is computed, the depth and velocity are

calculated in one of three ways: weirs, rating curves, and Manning equations. The

program decides among these options in the following manner:

If a weir height is entered, the weir option is implemented.

If the weir height is zero and a roughness coefficient is entered (n), the Manning

equation option is implemented.

If neither of the previous conditions are met, Q2K uses rating curves.

3.5.3

Travel Time

The residence time of each reach is computed. The residence time of each reach

are then accumulated to determine the travel time from the headwater to the

downstream end of reach i.

3.5.4

Longitudinal Dispersion

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3.6 Temperature model

The heat balance takes into account heat transfers from adjacent reaches, loads,

abstractions, the atmosphere, and the sediments. (Figure-3.5)

Figure 3.5 Heat balance

Reach has a particular temperature. There are some sources as an input into reach.So that, the net heat load came from point and non-point sources into reach. Other

heat flux is the air-water heat flux and the sediment-water heat flux. The specific heat

of water is used in model equations.

3.6.1

Surface Heat Flux

Surface heat exchange is modeled as a combination of five processes (Figure-3.6)

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atmosphere which is attenuated by atmospheric transmission, cloud cover, shade, and

reflection,

b) Atmospheric longwave radiation; the downward flux of longwave radiation from

the atmosphere is one of the largest terms in the surface heat balance. The

atmospheric longwave radiation model is selected on the Light and Heat worksheet

of QUAL2K. Three alternative methods are available: The Brutsaert equation,

Brunts equation (an empirical model that has been commonly used in waterqualitymodels), Koberg

c) Longwave back radiation from the water; it includes the back radiation from the

water surface.

d) Conduction and convection; conduction is the transfer of heat from molecule to

molecule when matter of different temperatures are brought into contact. Convection

is heat transfer that occurs due to mass movement of fluids. Both can occur at the air-

water interface.

e) Evaporation; evaporation can cause heat loss.

3.6.2

Sediment-Water Heat Transfer

There is a heat balance for bottom sediment underlying water. The air-water heat

flux and the sediment-water heat flux are important factor for heat transfer. The

effective thickness of the sediment layer affects the heat transfer. The soft, gelatinous

sediments found in the deposition zones of lakes are very porous and approach the

values for water. Some very slow, impounded rivers may approach such a state.

However rivers will tend to have coarser sediments with significant fractions of

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3.7 Constituents of the Model

3.7.1

Constituents and General Mass Balance

The model constituents are listed in Table 3.1

Table 3.1 Model state variables

* mg/L g/m3

For all but the bottom algae, a general mass balance for a constituent in a reach is

written as Figure 3.7

Variable Symbol Units*

Conductivity s mhosInorganic suspended solids mi mgD/LDissolved oxygen o mgO2/LSlowly reacting CBOD cs mgO2/LFast reacting CBOD cf mgO2/LDissolved organic nitrogen no gN/LAmmonia nitrogen na gN/LNitrate nitrogen nn gN/LDissolved organic phosphorus po gP/LInorganic phosphorus pi gP/LPhytoplankton ap gA/LDetritus mo mgD/LPathogen x cfu/100 mLAlkalinity Alk mgCaCO3/LTotal inorganic carbon cT mole/LBottom algae ab gD/m2

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different purposes (watering the animal, irrigation, drinking wateretc.). The

dispersion coefficient is significant only in the x-direction and remains constant

within the system boundary.

The river flow and waste input are the only inflows into the system, and the river

flow and abstraction is the only outflow from the system. The volumetric flow of the

waste stream is negligible compared to the river flow. Interactions with sediments

through suspended solids are negligible.

The most significant variables in the system can be identified as the flow rate in

the river, the concentration of the pollution parameters in the point and non point

source, the waste input rate, the waste output rate, the reaction rate constants for the

various processes that the pollutant can undergo within the system, and the length of

the river system. Other variables can be the area of flow and the velocity of flow in

the river. Some of the environmental processes that the pollutant can undergo within

the system, such as adsorption, desorption, volatilization, hydrolysis, photolysis,

biodegradation, and biouptake. These are depicted in Figure 3.8

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3.7.2

Reaction Fundamentals

Biochemical Reactions; the following chemical equations are used to represent the

major biochemical reactions that take place in the model (Stumm and Morgan 1996):

Plant Photosynthesis and Respiration:

Ammonium as substrate: P106CO2+ 16NH4

++ HPO2-4 + 108H2O C106H263O110N16P1+ 107O2+ 14H+

R

Nitrate as substrate:

Nitrification:

Denitrification:

Note that a number of additional reactions are used in the model such as those

involved with simulating pH and unionized ammonia. Stoichiometry of Organic

Matter; the model requires that the stoichiometry of organic matter (i.e., plants and

detritus) be specified by the user. The following representation is suggested as a first

approximation (Redfield et al.1963, Chapra 1997),

mgA 1000 : mgP 1000 : mgN 7200 : gC 40 : gD 100 (62)

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a) Oxygen Generation and Consumption

The model requires that the rates of oxygen generation and consumption be

prescribed. If ammonia is the substrate, the following ratio (based on Equation-1) can

be used to determine the grams of oxygen generated for each gram of plant matter

that is produced through photosynthesis.

107 moleO2(32gO2/moleO2)roca= = 2.69 gO2/gC (1)

106 moleC(12gC/moleC)

If nitrate is the substrate, the following ratio (based on Equation 63) applies

(2)

Note that Equation (2) is also used for the stoichiometry of the amount of oxygen

consumed for both plant respiration and fast organic CBOD oxidation.

For nitrification, the following ratio is based on Equation (3)

(3)

b) CBOD Utilization Due to Denitrification

As represented by Equation (4), CBOD is utilized during denitrification,

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Temperature effects on reactions; the temperature effect for all first-order

reactions used in the model is represented by

(where k(T) = the reaction rate [/d] at temperature T [oC] and = the temperature

coefficient for the reaction.)

3.7.3

Constituent Reactions

The mathematical relationships that describe the individual reactions and

concentrations of the model state variables (Chapra et al.2003).

1) Conservative substance

2) Phytoplankton: Phytoplankton increase due to photosynthesis. They are lost via

respiration, death, and settling. Phytoplankton photosynthesis is a function of

temperature, nutrients, and light. Three models are used to characterize the impact of

light on phytoplankton photosynthesis: Half Saturation (Michaelis-Menten) light

model, Steeles function, Smiths equation.

3) Bottom Algae: Bottom algae increase due to photosynthesis. They are lost via

respiration and death.

4) Detritus: Detritus or particulate organic matter (POM) increases due to plantdeath. It is lost via dissolution and settling

5) Slowly reacting CBOD (cs): Slowly reacting CBOD increases due to detritus

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formulations are used to represent the oxygen attenuation: Half Saturation,

Exponential, Second-order half Saturation

7) Dissolved organic nitrogen (no): Dissolved organic nitrogen increases due to

detritus dissolution. It is lost via hydrolysis.

8) Ammonia Nitrogen (na): Ammonia nitrogen increases due to dissolved organic

nitrogen hydrolysis and plant respiration. It is lost via nitrification and plantphotosynthesis.

9) Unionized ammonia: The model simulates total ammonia. In water, the total

ammonia consists of two forms: ammonium ion, NH+4, and unionized ammonia,

NH3. At normal pH (6 to 8), most of the total ammonia will be in the ionic form.

However at high pH, unionized ammonia predominates.

10) Nitrate nitrogen (nn). Nitrate nitrogen increases due to nitrification of ammonia.

It is lost via denitrification and plant photosynthesis.

11)Dissolved organic phosphorus (po): Dissolved organic phosphorus increases dueto dissolution of detritus. It is lost via hydrolysis.

12)Inorganic phosphorus (pi): Inorganic phosphorus increases due to dissolved

organic phosphorus hydrolysis and plant respiration. It is lost via plant

photosynthesis.

13)Inorganic suspended solids (mi): Inorganic suspended solids are lost via settling.

14) Dissolved oxygen (o): Dissolved oxygen increases due to plant photosynthesis. It

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15)Pathogen (x): Pathogens are subject to death and settling.

16)pH

17)Total inorganic carbon (cT): Total inorganic carbon concentration increases due

to fast carbon oxidation and plant respiration. It is lost via plant photosynthesis.

Depending on whether the water is undersaturated or oversaturated with CO2, it is

gained or lost via reaeration.

18)Alkalinity (alk): The present model accounts for changes in alkalinity due to

plant photosynthesis and respiration, nitrification, and denitrification.

3.7.4

SOD/Nutrient Flux Model

Sediment nutrient fluxes and sediment oxygen demand (SOD) are based on a

model developed by Di Toro (Di Toro et al. 1991, Di Toro et al.1993, Di Toro 2001).

The present version also benefited from James Martins (Mississippi State

University, personal communication) efforts to incorporate the Di Toro approach into

EPAs WASP modeling framework. A schematic of the model is depicted in Figure3.9. As can be seen, the approach allows oxygen and nutrient sediment-water fluxes

to be computed based on the downward flux of particulate organic matter from the

overlying water. The sediments are divided into 2 layers: a thin (~ 1 mm) surface

aerobic layer underlain by a thicker (10 cm) lower anaerobic layer. Organic carbon,

nitrogen and phosphorus are delivered to the anaerobic sediments via the settling of

particulate organic matter (i.e., phytoplankton and detritus). They are transformed by

mineralization reactions into dissolved methane, ammonium and inorganic

phosphorus. These constituents are then transported to the aerobic layer where some

of the methane and ammonium are oxidized The flux of oxygen from the water

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Figure 3.9 Schematic of SOD-nutrient flux model of the sediments

Diagenesis: The downward flux of particulate organic matter (POM) is converted

into soluble reactive forms in the anaerobic sediments. This process is referred to as

diagenesis. Stoichiometric ratios are then used to divide the POM flux into carbon,

nitrogen and phosphorus. Each of the nutrient fluxes is further broken down into

three reactive fractions: labile, slowly reacting and non-reacting. These fluxes are

then entered into mass balances to compute the concentration of each fraction in the

anaerobic layer.

Ammonium: Ammonium is in the aerobic layer and the anaerobic layers. The

concentration of total ammonium in the aerobic layer and the anaerobic layers are

used in equations. The ammonium concentration in the overlying water, the reaction

velocity for nitrification in the aerobic sediments, ammonium half-saturation

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of the mass transfer. Other mass transfer mechanism is between the water and the

aerobic sediments.

Nitrate: Mass balances for nitrate is in the aerobic and anaerobic layers. There are

denitrification processes and nitrate change into N2.The concentration of nitrate in

the aerobic layer and the anaerobic layers, the nitrate concentration in the overlying

water and the reaction velocities for denitrification in the aerobic and anaerobic

sediments are important factor for nitrate flux.

Methane: The dissolved carbon generated by diagenesis is converted to methane

in the anaerobic sediments. Because methane is relatively insoluble, its saturation can

be exceeded and methane gas produced. Dissolved methane corrected for gas loss

delivered to the aerobic sediments. The total anaerobic methane production flux is

expressed in oxygen equivalents. Flux of dissolved methane (expressed in oxygen

equivalents) that is generated in the anaerobic sediments and delivered to the aerobic

sediments.

SOD: The SOD is equal to the sum of the oxygen consumed in methane oxidation

and nitrification. The surface mass transfer coefficient depends on SOD. The SODin turn depends on the ammonium and methane concentrations

Inorganic phosphorus: Inorganic phosphorus is in the aerobic layer and the

anaerobic layers. The fractions of phosphorus are in dissolved and particulate form.

The concentration of total inorganic phosphorus in the aerobic layer and the

anaerobic layers, the inorganic phosphorus in the overlying water, the diagenesis flux

of phosphorus are important factor for phosphorus flux.

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CHAPTER FOUR

DEFINITION OF THE STUDY AREA

4.1 Tahtali Basin

The Tahtali Stream, which is one of major stream systems in Izmir, serves as an

important water resource for the area. (See figure 4.1) The river drainage area is 512

km2. The Tahtali Dam was built in the Tahtali Stream to supply drinking water. Raw

water is pumped from the Tahtali Dam to Gorece Water Treatment Plant. Treatment

Processes of the Plant are; aeration, pre-chlorination, coagulation and flocculation,

rapid sand filtration and chlorination. In this processes, general water quality

characteristics of the raw water is improved, so that good quality of drinking water is

produced. Tahtali Dam Reservoir, which is used as source of potable water, is

subject to contamination coming from domestic, industrial, livestock, and urban and

agricultural sources. The control measures of those pollution sources are being

undertaking. Yet, certain amount of contaminants reaches the tributaries and

reservoir. In this perspective, the waste water originated from domestic

establishments is conveyed to the outside of the basin borders by either sewer system

or by trucks. The major treatment facility that treats the waste water of Menderes

town is implemented and operated recently. The treated waste water is not disposed

into the basin; it is transported to the outside.

As stated above IZSU performing many pollution control measures towards the

decreasing of pollutant sources in the basin. However, those measures are basically

focused on the control of point sources. Therefore, it should be revealed that the

major existing pollution component is the non-point sources. Non-point sources are

generally originated from agricultural activities and rainfull-runoff characteristics

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36

Figure 4.1 Tahtal Dam Basin


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Absolute Protection Zone: There are 48 active and 49 inactive industries in this area

(DEU, 2000). Active industries are animal farm, foundry, oil, plastic, furniture,

agricultural products, petroleum, dye plants etc. These facilities are in the Gorece,

Golcukler, Kisikkoy, Oglananasi, Menderes, Demircikoy, Develikoy, Derekoy,

Akcakoy, YesilKoy. Inactive industries are animal farm, lumber, machine

production, mine, agricultural products, and petroleum .etc. These facilities are in

the Gorece, Golcukler, Karacaagac, Kisikkoy, Oglananasi, Menderes, Demircikoy,Develikoy, Derekoy, Akcakoy, YesilKoy.

Short Distance Protection Zone: There are 10 active and 7 inactive industries in this

zone (DEU, 2000). Active industries are Pinar Water and 9animal farms. These

facilities are in the Bulgurca, Degirmendere, Sasal village and Kuner. Inactive

industries are animal farms. These facilities are in the Bulgurca, Degirmendere and

Sasal village. These facilities dont take precautions, so they must be removed from

this area according to regulations.

Middle Distance Protection Zone: There are 12 active and 3 inactive industries in the

middle distance protection zone in Tahtali Basin (DEU, 2000). Active industries areanimal farm, machine production industry, mine industry and water industry. These

facilities are in the Bulgurca, Develikoy, Degirmendere and Sasal village. Inactive

industries are fattening shed, mining industry and cotton industry. They are in the

Degirmendere, Develikoy and Kuner. Regulations dont give permission these

facilities. The facilities are danger for the basin. Politeknik Machine Production

Industry has a waste water treatment plant. It can not be obtained any information

about process water of other facilities.

Long Distance Protection Zone: There are 284 active and 224 inactive industries in

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Belenbasi, Demircikoy, Yogurtcular, Sarnic, Develikoy, Kuner, Derekoy, Akcakoy,

Yesilkoy. Animal plants are not allowed in the area according to regulations. If

existing animal plants take some precautions, IZSU may permit these facilities.

Inactive industries are animal farms, metal, plastic, textile, furniture, agricultural

products, milk products, petroleum, bodywork, fodder, packing, spring water, ...etc.

They are in the Gorece, Gaziemir, Golcukler, Karacaagac, Kaynaklar, Kisikkoy,

Oglanansi, Menderes, Kiriklar, Belenbasi, Demircikoy, Yogurtcular, Sarnic,

Develikoy, Kuner, Derekoy, Akcakoy, Yesilkoy. A few of these facilities takenecessary precautions. Most of them cause increasing the pollution of the basin.

Some of these industries are operated by illegal ways. So there is not any information

about most of the industries.

4.3 Pollutant Sources of the Tahtali Basin

There are 43 tributaries in the basin. These creeks merge and reach the Tahtali

Dam. Pollutants can transport by rain, run-off and leakage into the creeks. Polluted

creeks affect the water quality of Tahtali Dam. Pollutant sources can be grouped as

point sources and non-point sources in the basin. The term point-source pollution

refers to pollutants discharged from one discrete location or point, such as anindustry or municipal wastewater treatment plant. The term non-point source

pollution refers to pollutants that cannot be identified as coming from one discrete

location or point. Non-point pollution is generally originated from agricultural

runoff.

Most of wastes are transported out of the basin. But, A little waste is still

discharged in the basin. And they reach and pollute the dam water. Some materials

used in agriculture for protection against harmful organisms may reach the creek by

surface run off and change the water quality of the basin

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Gorece Immigrant Residences has got a wastewater treatment plant. But, there are

some operation problems and its wastewater is discharged without refinement. It is

an important pollution source for the basin. Apparently, it can be seemed that

domestic wastewaters are considerable pollution sources in the basin. Their pollution

load can be computed by using population of the city.

There are active and inactive industries in the Tahtali Basin. Some of the

industries directly discharged wastewater into the streams. Some of them use ownwastewater as process water. 17 Industries have got wastewater treatment plants. One

of them in Kisikkoy discharged treated wastewater in the basin, the other industries

wastewater is transported out of the basin by using vehicles.

Some industries have considerable amount of wastewater. Pinar Water Industrys

wastewater is transported out of the basin (8m3/d). Polithecnic Machine Production

Industry has a water treatment plant (20m3/d). Menderes Municipality

Slaughterhouse (40m3/d), Tansas Meat Integration Plants (1760m3/d), Unal

Agricultural Productions (88m3/d), Gunkol (46 m3/d), Koytur Aegean Integration (12

m3/d), ESTIM Industry (450m3/d), Coban Meat Integration Plants (50 m3/d), SAN-

FA (45 m3/d), Ozkul (10m3/d), CD Textile (40m3/d), Nur Village Milk Products

(35m3/d), Tufekci Agricultural Products (12m3/d) are other important facilities. A

treated wastewater result from Tansas Meat Integration Plants is discharged into

Gokdere stream and it can reach Izmir Bay. ESTIM wastewater is discharged in the

basin after they refined in the wastewater treatment plant. DHMI Adnan Menderes

Airport wastewater is unloaded into a canal, then it is reached to one of the stream

near the Golcukler. Nur Village Milk Products wastewater is discharged into the

sewer system in Torbali Ayrancilar Municipality. Tufekci Agricultural Products

wastewater is collected in a septic tank, and then it is transported out of the basin by

vehicles

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Table 4.1 Industries which have wastewater treatment plant (DEU,2000)

Industry Treatment Capacity (m3/day) Amount of Treated Water (m3/day)

DHMI Adnan Menderes Airport 1000 1000Tansas Meat Integration Plants 1760 1760

ESTIM Industry 750 0

Gunkol 180 46

Coban Meat Integration Plants 50 0

Sanfa 45 45

Ozkul Clothing 40 10

CD Textile 40 0

Nur Village Milk Products 35 35

Tokkullar Export 25 0

Polithecnic Machine Production 20 0

Meko Metal 20 0

Artkiy Leather 20 0

Egemer Automative 15 0

Tufekci Agricultural Products 20 12

Data in table-4.1 are taken place in sources of Izmir Sewage and Water Authority

(IZSU). Industries in which treated water is 0 m3/d and domestic wastewater are

collected in septic tank and transported out of the basin. If we compare amount of

water which collected in septic tanks and treatment capacity of the plant, there is a

big difference between them. It means that some industries wastewaters are nottransported out of the basin.

Tahtali Dam is one of the important water sources in Izmir in 2000. Some

agricultural facilities affect badly the quality of dam water in basin protection area.

Land use distribution in dam protection basin is given in Appendix-F. Farmers which

have got small land deal with cattle, especially sheep. Farmers have large land work

on vegetable and cattle. This complex area is 70 percent of the total land. Main

facilities on agriculture in the basin are tobacco, cotton and greenhouse. There are I.

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Many kind of harmful organisms and diseases increase on the soil because of

continuously same products production in the greenhouses. So that, producer use

some chemicals for disinfections of the soil. They are not only expensive, but also

dangerous to environment. Extreme pesticide is used for this purpose. These

chemicals can reach the dam by infiltration or surface run-off into the creeks. Dam

water can be easily contaminated with these materials. Thus, one of the important

water sources in Izmir will become useless in the future.

IZSU sampled dam water for controlling if it is contaminated by pesticide

residues. Analysis are started in 1995 and ended in 2000. Apparently, these analyses

have exposed that dam water was clean about pesticide. But this result doesnt mean

that Tahtali dam is completely pure and remains pure after that. As a result,

agricultural residues affect the water quality in the basin. These residues reach the

creeks by infiltration or surface run-off. They are non-point (diffuse) sources in the

basin. And their effects are very considerable on water quality.

Solid waste sources are industrial, domestic and animal wastes. Solid waste

quantity is related to process in the industry. If industry has a treatment plant, its

sludge is decomposed as a solid waste. Solid wastes results from animal resemble

manure characteristics. If they arent take away form the basin, they affect the

ground and surface water. Or they may be used as fertilizer but also their effect on

water continues. For this purpose, animal wastes are collected on a special area

which doesnt allow passing the leakage into the ground water. Industrial and

domestic solid wastes in the basin are picked by the related municipalities. They are

taken to loading ramp. And then, these collected wastes are transported to

Harmandali solid waste dumping area by Izmir Municipalities (DEU, 2000). In

addition, there is no any solid waste problem in the basin if there isnt illegal

dumping

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4.4Menderes Sehitoglu Creek

In this study, examined stream will be Menderes Sehitoglu Creek which is a part

of Tahtali Basin (Figure 4.1). It is on the east of the basin. There are some

settlements around the creek. They are Menderes, Akcakoy, Derekoy and Develi.

Menderes is the biggest settling among them. There is agriculture, Industry and

cattle-dealing improved in Menderes. Akcakoy and Derekoy are small settlements.

Their agriculture areas are very small. So, cattle dealing may be developed in these

areas. Develi is nearest village to the dam reservoir. Develi doesnt have agriculture

area as big as the Menderes.

Figure 4.2 Menderes Sehitoglu Creek

As seem in Figure 4.2, there arent too many settling and facilities. It means that

there arent any big pollution sources if the wastes are transported out of the basin.

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Menderes is the most developed city among them. There is a sewage system

problem. Leaked septic tanks are still used. And there is not any information about

quantity of septic tanks in Menderes. Their leakage causes decreasing the stream

water quality. At the same time, Menderes is an industry city. There are some

facilities. They are Ali Galip Food Ind. (5 m3/d), Gozde Ind. (timber..) , Aclm Box

Ind. (0,7 m3/d), Beser Polyester (0,7 m3/d), Tosun Metal (0,2 m3/d), Menderes

Municipality Slougterhaouse (40 m3/d) (DEU,2000). According to regulations, their

wastes must be taken away from the basin. In fact, some of them are not removed.

So, they threaten the water quality of Menderes Sehitoglu Creek.

Menderes is also a big settling place. Its population is 15750 capita [State

Statistics Institue (DIE), 1997]. If we take into consideration leaked septic tanks with

high population, there is important quantity of the pollution on water. There are other

houses near the Menderes as known Gumus Mestanli Houses. Its population is 8400

capita (DIE, 1997). It means their pollution load is very high.

Derekoy and Akcakoy is on the stream where is connected the Menderes

Sehitoglu Creek. Akcakoy population is 331 capita (DIE, 1997). There arent any

industrial facilities. Akcakoys agricultural area is 3500da (DEU, 2000). There is

mostly produced oil, in trace quantities citrus fruits, vegetable, cereals, vineyards,

tobacco and cotton. There may be cattle dealing facilities. Their agriculture areas are

too small and their pollutants are not important quantities. So that, this pollution

source is familiar to domestic wastewater.

Develi is on the place where Menderes Sehitoglu Creek joins the Tahtali Stream.

Develi doesnt have agriculture area as big as the Menderes. Its area is 7515da

(DEU 2000) Products are mostly tobacco and cereals Develi is smaller settling than

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Other point source is coming from in a settling place, Oglananasi. This is one of

tributaries in the basin. Its population is 1877 capita (DIE, 1997). Its agricultural area

is 24000da (DEU, 2000). Products are cereals, tobacco and cotton. Quantity of

domestic wastewater must be very little because of its developed infrastructure

system. Last point source is also a tributary which is upper part of Tahtali Stream.

There are many creeks connected each other. There are many industries, agricultural

area, animal farms. Therefore, this pollution source is very impressive compared to

others.

4.6Existence Data in the Studied Creek

Some information were obtained from IZSU, DSI and related studied investigated

before. These will be presented in this part and Appendix-A. Total river drain area is

512 km2 (DSI, 1997). Water flow in main stream is 4,4 m3/s in Spring months.

Menderes Sehitoglu Creek flows are computed by using Appendix-A (Hydrology

and Hydraulic Characteristics of Tahtali Stream). Stream was named on the studied

creek. (see Figure-4.3)

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W t Fl D t


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Water Flow Data

13 streams drain area: 6,10 km2 15 streams drain area : 33,5 km2

14 streams drain area: 20,4 km2 16 streams drain area : 17,7 km2(IZSU,2000)

According to these data;

Q13= 0,052 m3/s Q14= 0,18 m

3/s Q17= 3,73 m3/s

Q15= 0,522 m3/s Q16= 0,15 m

3/s QHeadwater= 0,12 m3/s

Meteorological Data

Some data are obtained from meteorology [State Meteorological Works (DMI),

2004]. They are related with wind speed, relative humidity and temperature. This

information is used in modeling part. Relative humidity is used for computing dew-

point temperature. These data are average of the spring months values. Because in

spring flow is high, in summer most of creeks dry. So, spring values will be used to

constitute different scenarios. These meteorological data are;

Relative humidity : %66 Temperature : 20 oC

Wind speed : 5 m/s Cloud cover : 50 %

Parameters Data

Seven water quality stations are placed in the basin for decreasing the pollution

risks. Data about water quality are obtained from these stations. By using these data,

water source can be protected against the bad conditions. Some scenarios can be

developed and taken some precautions. Data are important to decide that this water

source is or not suitable for usage purpose (drinking irrigation watering )

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Table 4 2 Quality parameters


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Table 4.2 Quality parameters

Quality Parameters

1 Fluoride (mg/L) 25 Magnesium (mg/l)2 Total phosphorus(mg/L) 26 Bicarbonate (mg/L)

3 Biological Oxygen Demand (mg/L) 27 Chloride (mg/L)

4 Phosphate (mg/L) 28 Organic Matter (mg/L)

5 Phosphate Phosphorous (mg/L) 29 Sulfate

6 Ammonia Nitrogen (mg/L) 30 Lead (mg/L)

7 Nitrite Nitrogen (mg/L) 31 Total Chromium (mg/L)

8 Nitrate Nitrogen (mg/L) 32 Chromium (6)(mg/L)

9 Suspended Solid (mg/L) 33 Zinc (mg/L)

10 Total Dissolved Solid (mg/L) 34 Mercury (mg/L)

11 Dissolved Oxygen (mg/L) 35 Cadmium(mg/L)

12 Chemical Oxygen Demand (mg/L) 36 Copper (mg/L)

13 Sodium (mg/L) 37 Boron (mg/L)

14 Potassium (mg/L) 38 Iron (mg/L)

15 Color (Pt/Co) 39 Nickel (mg/L)

16 Phenol Matter (mg/L) 40 Barium (mg/L)

17 Temperature (C) 41 Aluminum (mg/L)

18 Emulsified oil and Grease(mg/L) 42 Arsenic (mg/L)

19 Oxygen Saturation (%) 43 Manganese (mg/L)

20 Methyl Blue Active (mg/L) 44 Total Coliform (100ml)EMS

21 PH 45 E. Coli 37C / ml'de

22 Conductivity (umhos) 46 Fecal Coliform (100ml) EMS

23 Total Hardness (Fr) 47 Fecal Streptococcus 100ml

24 Calcium (mg/l)

There is two stations for sampling on the studied creek (Figure 4.3). Somepollutant parameters had been examined by IZSU between 1996 and 2000. Sampling

was made once or twice a month. These parameters are listed in Table-4.2. Their

statistical analyses are going to be evaluated in next part. There will be descriptive

statistics related to sampling value.

Headwater Data

There isnt a water quality stations on the headwater. So, there isnt any clear

i f ti b t h d t lit B t it i k th t th t i d t i l

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Table 4 3 Headwater quality [Uslu & Turkman 1987]


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Table 4.3 Headwater quality [Uslu & Turkman, 1987]

Parameter Unit Parameter Unit

PH 7,5 Fluoride mg/L 0,44

Conductivity umhos 400 Total Phosphorus mg/L 0

Salt S%O 0 Total Cyanide mg/L 0

Total Hardness Fr 27 Biochemical Oxygen Demand mg/L 4

Calcium mg/L 84 Phosphate mg/L 0,04

Magnesium mg/L 17 Phosphate Phosphorous mg/L 0,02

Bicarbonate mg/L 270 Ammonium Nitrogen mg/L 0,02

Chloride mg/L 25 Nitrite Nitrogen mg/L 0,002

Organic Matter mg/L 0,8 Nitrate Nitrogen mg/L 2,5

Ammonia yok Suspended Solid mg/L 48Free Chlorine mg/L 0 Total Dissolved Solid mg/L 50

Sulfate mg/L 18 Dissolved Oxygen mg/L 9

Sulphur mg/L 0 Chemical Oxygen Demand mg/L 10

Lead mg/L 0,003 Sodium mg/L 12

Total Chromium mg/L 0,001 Potassium mg/L 1

Chromium (+6) mg/L 0,001 Selenium mg/L

Zinc mg/L 0,09 Color Pt/Co 5

Mercury mg/L 0 Phenol mg/L

Cadmium mg/L 0 Temperature C 18Copper mg/L 0,003 Emulsion Oil and Grease mg/L 0,03

Boron mg/L 0,17 Oxygen Saturation % 80

Iron mg/L 0,1 Methyl Blue Active Matter mg/L 0,03

Nickel mg/L 0,005 Chlorine ppm 0

Barium mg/L 0,07 Total Coliform 100 ml / EMS 100,000

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CHAPTER FIVE


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CHAPTER FIVE

WATER QUALITY VARIABLES FOR MODELING

5.1 General

Water quality data are needed to delineate the general nature and trends in water

quality characteristics, the effects of natural and man-made factors upon the general

trends in water quality. So that, water quality monitoring is essential for water quality

management in a region. Water quality monitoring comprises all sampling activities

to collect and process data on water quality for the purpose of obtaining information

about the physical, biological and chemical properties of water. Collected data are

stored and analyzed to produce the expected information. At the end of the analyses,

it is revealed which quality parameters get worse. And, related model is used for

monitoring of these variables. Therefore, selection of variable is important for the

model. Because there are many variables. Water quality variables are used to get

information about water quality in a river basin. Water quality variables change

temporary and spatially. The basic and the simplest attempt for determination of any

data effects is the graphical representation. General trends of the water quality can be

monitored by using of graphics. These spatial distributions of the variables can be

used for prediction of water quality. It is revealed which variables are important for

the river basin. Second step of the selection of water quality variables is applying

statistical analyses. Statistics are used to express the data in terms of numbers and/or

equations in summary form. Results of the statistic analyses are used to evaluate the

water quality. Needed variables for the model can be selected by

modeling of water quality

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