MODELING OF WATER QUALITY IN A DRINKING WATER BASIN

Transcription

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DOKUZ EYLUL UNIVERSITY
GRADUATE SCHOOL OF NATURAL AND APPLIED
SCIENCES
MODELING OF WATER QUALITY IN A
DRINKING WATER BASIN
by
Sündüz UTKU
January, 2005
İZMİR
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A Thesis Submitted to the
Graduate School of Natural and Applied Sciences of Dokuz Eylül University
In Partial Fulfillment of the Requirements for the Degree of Master of Science
in
Environmental Engineering, Applied Environment Technology Program
by
Sündüz UTKU
January, 2005
İZMİR
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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 Sündüz UTKU under
supervision of Prof. Dr. Necdet ALPASLAN and that in our opinion it is fully
adequate, in scope and in quality, as a thesis for the degree of Master of Science.
Supervisor
(Jury Member)
(Jury Member)
Approved by the
Graduate School of Natural and Applied Sciences
Prof.Dr. Cahit HELVACI
Director
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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 Hülya BOYACIOĞLU 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.
Sündüz Bayraktar UTKU
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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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İÇME SUYU HAVZASINDA SU KALİTESİNİN
MODELLENMESİ
ÖZET
Dünyadaki birçok
içme suyu havzası antropolojik aktivitelerden dolayı
kirlenme riski taşımaktadır. Bu amaçla her havza için etkili bir havza yönetim planı
geliştirmek zorunlu hale gelmiştir. İyi bir yönetim planı için, havzadaki su kalite
parametrelerinin zamana ve yere göre tanımlanması gerekmektedir. Bu yüzden
bilgisayar destekli simulasyon modelleri geliştirilmiş ve geniş bir alanda uygulamaya
başlanmıştır. Daha çok yüzeysel suların modellenmesinde kullanılan QUAL2K Su
Kalitesi Modeli sunulan çalışma kapsamında incelenmiş ve uygulanmıştır.
QUAL2K, su kalitesini modelleyerek oluşan kirliliğin boyutlarını ve kirletici
kaynakları belirlemede kullanılan bir bilgisayar programı paketidir. Bu tezde
öncelikle model tanıtılmış, modelin çalışma prensibi, girdileri ve özellikleri
açıklanmıştır. Daha sonra İzmir’in en önemli içme suyu kaynaklarından biri olan
Tahtalı Havzası’nın bir parçası üzerinde modelleme çalışması yapılmıştır. Bu
havzanın seçilmesinin nedeni, havzada çok sayıda noktasal ve noktasal olmayan
kirletici kaynağın bulunmasıdır. Bu amaçla, modellenen koldaki kirletici kaynaklar
belirlenmiş ve bu kirletici kaynaklara göre senaryolar oluşturulmuştur. Bu olası
senaryolardan elde edilen sonuçlar, havzanın bu kolu üzerindeki kirlenmenin
boyutunu göstermektedir. Modelleme çalışmaları bu özelliği ile karar verici
mekanizmaların daha çok ilgisini çekmektedir. Bu nedenle, sunulan çalışma ile,
havza yönetiminde karar vericiye önemli bir araç sağlanmış olacaktır.
Anahtar Sözcükler : Havza Yönetimi, Yüzeysel 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
ÖZET ….. ...............................................................................................................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 .............................................................4
2.2 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
3.4.1 QUAL2K WORKSHEET .................................................................15
3.4.2 HEADWATER WORKSHEET.........................................................15
3.4.3 REACH WORKSHEET....................................................................15
3.4.4 METEOROLOGY AND SHADING WORKSHEET ........................16
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3.4.5 RATES WORKSHEET.....................................................................17
3.4.6 LIGHT AND HEAT WORKSHEET.................................................18
3.4.7 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 .....................................................23
3.6 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
4.5 POLLUTANT SOURCES ON MENDERES SEHITOGLU CREEK .........43
4.6 EXISTENCE DATA IN THE STUDIED CREEK .....................................45
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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 TO THE STUDY
AREA .......................................................................................54
6.1 GENERAL ................................................................................................54
6.2 HEADWATER DATA ..............................................................................55
6.3 REACH DATA..........................................................................................59
6.4 METEOROLOGY AND SHADING DATA ..............................................61
6.5 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
APPENDIX A : WATER QUALITY OBSERVATIONS STATIONS DATA
SETS…. .....................................................................................116
APPENDIX B : GRAPHICAL PRESENTATION OF WATER QUALITY
VARIABLES IN SEHITOGLU CREEK .....................................119
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APPENDIX C : WATER QUALITY CLASSIFICATION FOR SURFACE
WATERS ...................................................................................125
APPENDIX 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
world’s 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 should
be 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
recent years, mathematical simulation models have been consulted to solve the water
pollution problem in a basin. By using suitable mathematical modeling, many water
sources can be evaluated and elevated qualitatively and quantitatively. Thus, it is
intended in this study to present the application of a water quality model (QUAL2K
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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 be
attributed 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
provide better understanding of the water quality processes in the creek. These
modeling studies are a powerful tool in the integrated basin management. It is
obvious that, the model is only a tool for preparing the water resource plan. The
model is not an objective for the management planning.
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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 about
Qual2k 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
2.1 General 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
of the modeling is evaluation of the environmental and ecological effects of various
reservoir operations and water management alternatives. It is extremely important to
recognize that the models or software packages only provide a framework for the
analyses. Data specific to the watershed, industrial plants, and management scenarios
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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 base
against 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,
etc.) that the model can predict (WBG, 1998). For indicators of aerobic status, such
as biochemical oxygen demand (BOD), dissolved oxygen, and temperature, simple,
well-established models can be used to predict long-term average changes in rivers,
streams, and moderate-size lakes. The behavior of these models is well understood
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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 be
predicted 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).
Comment
Criterion
Single-plant or regional focus
Static or dynamic
Stochastic or deterministic
Type of receiving water
(river, lake, or estuary)
Simpler models can usually be used for single-plant
“marginal” effects. More complex models are
needed for regional analyses.
Static (constant) or time-varying outputs.
Stochastic models present outputs as probability
distributions; deterministic models
are point-estimates.
Small lakes and rivers are usually easier to model.
Large lakes, estuaries, and large
river systems are more complex.
Water quality parameters
Usually decreases as discharge increases. Used as a
Dissolved oxygen
water quality indicator in most water quality
models.
A measure of oxygen-reducing potential for
Biochemical oxygen demand waterborne discharges. Used in most
(BOD)
water quality models.
Often increased by discharges, especially from
Temperature
electric power plants. Relatively easy
to model.
Reduces dissolved oxygen concentrations and adds
Ammonia nitrogen
nitrate to water. Can be predicted
by most water quality models.
Increases with pollution, especially nitrates and
Algal concentration
phosphates. Predicted by moderately
complex models.
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Coliform bacteria
Nitrates
Phosphates
Toxic organic compounds
Heavy metals
An indicator of contamination from sewage and
animal waste
A nutrient for algal growth and a health hazard at
very high concentrations in drinking
water. Predicted by moderately complex models.
Nutrient for algal growth. Predicted by moderately
complex models.
A wide variety of organic (carbon-based)
compounds can affect aquatic life and may
be directly hazardous to humans. Usually very
difficult to model.
Substances containing lead, mercury, cadmium, and
other metals can cause both
ecological and human health problems. Difficult to
model 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
parameters are usually simpler than stochastic predictions of the probability
distributions of those parameters.
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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 than
large 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 nonconservative 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 watershedscale 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 a
watershed-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,
rivers, lakes and reservoirs. Pollutants, which are used in the model, nitrogen,
phosphorus, pesticides, organics, and BOD-DO interactions (Frey et al. 2002).
WARMF, Watershed Analysis Risk Management Framework (WARMF) is an
integrated model that predicts changes in water quality due to point and nonpoint
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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 QUAL2K’s ability to simulate the conversion of algal death to BOD, fixed
plant DO, and the denitrification (Park et al. 2001). One of the major inadequacies of
the QUAL2E model is the lack of provision for conversion of algal death to BOD,
which is autochtonous source of organic matter. The maximum numbers of reaches,
computational elements, and junctions are limited in currently available version of
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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
model, comparing the model results to actual monitoring data to see how well they
mimic reality, and adjusting certain appropriate input data until the model results
reasonably match the monitoring data (Frey et al. 2002). Verification involves
splitting data into two sets. The modeler would create a calibrated model using one
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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. It’s
more important to have enough of the right kind of data over a particular time period;
it’s 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 USEPA’s 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 Excel’s macro language: Visual Basic for Applications or
VBA. The Excel Workbook’s 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
Applications (VBA). Excel is used as the graphical user interface. Model
segmentation, Q2E segments the system into river reaches comprised of equally
spaced elements. In contrast, Q2K uses unequally-spaced reaches. In addition,
multiple loadings and abstractions can be input to any reach. Carbonaceous BOD
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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 river’s 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
purpose of the use (Brown and et al. 1987). QUAL2E compiles the features of the
available QUAL2 versions on which was added the uncertainty analysis options
(Brown, 1986; Brown et al. 1987). The last version of qual is QUAL2K.
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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-effluentdischarge 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
thus both the wasteload and the streamflow vary significantly over time. Both types
of variation may deviate significantly from the assumptions of QUAL2K. (Shanahan
et al., 1998)
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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 system’s
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 river’s 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
and leave the other blank or zero. If the model detects a blank or zero value for the
Manning n, it will implement the rating curves. Otherwise, the Manning formula will
be solved. These options need some data for using them.
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For Rating Curves:
Velocity coefficient. (a),
Depth coefficient. (ά),
Velocity exponent. (b),
Depth exponent. β
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
system’s reaches. Dew-Point temperature worksheet; this worksheet is used to enter
hourly dew-point temperatures (degrees Celcius) for each of the system’s reaches.
Wind speed worksheet; This worksheet is used to enter hourly wind speeds (meters
per second) for each of the system’s reaches.Cloud cover worksheet; this worksheet
is used to enter hourly cloud cover (% of sky covered) for each of the system’s
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reaches.Shade worksheet; this worksheet is used to enter hourly shading for each of
the system’s reaches.
3.4.5 Rates Worksheet
This worksheet is used to enter the model’s 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), bottom
algae, 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
river’s depth and velocity (Covar 1976)), O’Connor-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 for NH4 nitrification. Suggested value: 4.57 gO2/gC.
Oxygen inhibition C oxidation model. Options are: Half-saturation,
Exponential, Second order.
Oxygen inhibition C parameter.
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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 system’s 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
υgA/L), nonlinear chlorophyll light extinction (according to Riley (1956) 0.054/m
(υgA/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 system’s 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
inflow should be left blank. If there is a point inflow, a value for flow (m3/s) must be
entered.
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3.4.8 Diffuse Sources Worksheet
This worksheet is used to enter information related the system’s diffuse (i.e., nonpoint) 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
system’s 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
et al.2003).
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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 the
simulation date. Notice that this plot also displays the oxygen saturation as a dashed
line. (see figure 3.1)
Figure 3.1 The plot of dissolved oxygen versus distance downstream in km.
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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.
Figure 3.2 The diel plot of the dissolved oxygen versus time of day.
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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).
Figure 3.4 Reach flow balance
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Qi+1 outflow from reach i into reach i + 1 [m3/d], Qi–1 = 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,i is 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 lengthweighted 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
Two options are used to determine the longitudinal dispersion for a boundary
between two reaches. First, the user can simply enter estimated values. If the user
does not enter values, a hydraulics based formula is employed to internally compute
dispersion based on the channel’s hydraulics (Fischer et al. 1979).
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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)
Figure 3.6 The components of surface heat exchange
a) Solar shortwave radiation at the water surface; the model computes the amount
of solar radiation entering the water at a particular latitude and longitude on the
earth’s surface. This quantity is a function of the radiation at the top of the earth’s
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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,
Brunt’s equation (an empirical model that has been commonly used in waterquality
models), 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 airwater 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
sands, gravels and stones. Upland streams can have bottoms that are dominated by
boulders and rock substrates. These natural sediments have some special thermal
properties. For example, solid material in stream sediments leads to a higher
coefficient of thermal diffusivity than that for water or porous lake sediments.
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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
Variable
Conductivity
Inorganic suspended solids
Dissolved oxygen
Slowly reacting CBOD
Fast reacting CBOD
Dissolved organic nitrogen
Ammonia nitrogen
Nitrate nitrogen
Dissolved organic phosphorus
Inorganic phosphorus
Phytoplankton
Detritus
Pathogen
Alkalinity
Total inorganic carbon
Bottom algae
* mg/L ≡ g/m3
Symbol
Units*
s
mi
o
cs
cf
no
na
nn
po
pi
ap
mo
x
Alk
cT
ab
υmhos
mgD/L
mgO2/L
mgO2/L
mgO2/L
υgN/L
υgN/L
υgN/L
υgP/L
υgP/L
υgA/L
mgD/L
cfu/100 mL
mgCaCO3/L
mole/L
gD/m2
For all but the bottom algae, a general mass balance for a constituent in a reach is
written as Figure 3.7
Figure 3.7 Mass balance
Point source and non-point source concentrations are used for computing the
external load. In the river system, there is may be some abstraction point to use for
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different purposes (watering the animal, irrigation, drinking water…etc.). 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
Figure 3.8 Some environmental processes
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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:
P
106CO2 + 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)
It should be noted that chlorophyll a is the most variable of these values with a
range of approximately 500-2000 mgA (Laws and Chalup 1990, Chapra 1997).
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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,
(4)
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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 θ = 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, Steele’s function, Smith’s 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 plant
death. It is lost via dissolution and settling
5) Slowly reacting CBOD (cs): Slowly reacting CBOD increases due to detritus
dissolution. It is lost via hydrolysis.
6) Fast Reacting CBOD (cf): Fast reacting CBOD is gained via the hydrolysis of
slowly-reacting CBOD. It is lost via oxidation and denitrification. Three
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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 plant
photosynthesis.
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 due
to 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
is lost via fast CBOD oxidation, nitrification and plant respiration. Depending on
whether the water is undersaturated or oversaturated it is gained or lost via
reaeration.
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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 Martin’s (Mississippi State
University, personal communication) efforts to incorporate the Di Toro approach into
EPA’s WASP modeling framework. A schematic of the model is depicted in Figure
3.9. 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
required for these oxidations is the sediment oxygen demand (Chapra et al.2003).
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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
constant, the dissolved oxygen concentration in the overlying water , oxygen halfsaturation constant and the diagenesis flux of ammonium are the other important
factor for ammonium flux. The solids concentration in layer must be known because
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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 SOD
in 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.
34
35
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
determine the magnitude of non-point sources in certain extend. As a result, Tahtali
basin is polluted as an obvious ratio. To protect the Tahtali basin and dam, IZSU
struggles with the pollution by using public efforts and national regulations. For this
purpose; nationalization continue, absolute protection and reservoir zone surround by
hedge to stop the illegal agricultural and cattle dealing activities, afforestation works
35
35
36
Figure 4.1 Tahtalı Dam Basin
36
37
keep on, waste water subjected from domestic and industrial is transported out side
of the basin by building a suitable infrastructure system [Izmir Sewage and Water
Authority (IZSU),2004]. Other protection way of IZSU is introducing of protection
zones in the basin as legal. In this case, Basin Protection Regulation is published.
According to Basin Protection Regulations, four type protection zones are
introduced. These are;
Absolute Protection Zone; It includes the area of 300m distance from maximum
water level. None of structure can built in this zone. Only infrastructure system and
water supply projects can be allowed.
Short Distance Protection Zone; It includes the area of 700m distance from the
border of Absolute Protection Zone. None of tourism, settlement and industrial
facilities are allowed.
Middle Distance Protection Zone; It includes the area of 1 km distance from Short
Distance Protection Zone. Industrial facilities, animal plants, all of the storage plants,
settlement area and greenhouse facilities are not allowed.
Long Distance Protection Zone; It includes the area which is another part of the
water collection basin. Wastewater comes from existing settlement place are
collected in an isolated storages and transported to the outside of the basin. None of
industrial facilities are allowed. If an industrial plant exists before the regulation, it
may be allowed to proceed, provided that, its wastewater characteristics must be as
domestic sewage.
4.2 Existing Condition on the Protection Zones
After these descriptions of the protection zones, Tahtali Dam Basin Protection
Zones can be explained as existing state. Unfortunately, there are many houses,
industrial and agricultural activities. Some of the plants are inactive; some of them
are active state.
38
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 don’t 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 are
animal 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 don’t 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
the long distance protection zone in Tahtali Basin (DEU, 2000). Active industries are
animal farms, metal, plastic, spring water, dye plants…etc. Restaurants are other
important factor for pollution of the basin. They are in the Gorece, Gaziemir,
Golcukler, Karacaagac, Kaynaklar, Kisikkoy, Oglanansi, Menderes, Kiriklar,
39
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 take
necessary 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 an
industry 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.
Some village in the basin have sewerage, some of them haven’t. There isn’t
enough treatment plant for domestic wastewater in the basin. So, domestic waste
water generally discharged into the streams. These are very important point sources.
40
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 own
wastewater 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 Industry’s
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), SANFA (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.
41
Table 4.1 Industries which have wastewater treatment plant (DEU,2000)
Treatment Capacity (m3/day)
Amount of Treated Water (m3/day)
DHMI Adnan Menderes Airport
1000
1000
Tansas 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
Industry
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 not
transported 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.
class lands around the Tahtali Creek. It means these lands are productive. On the east
of the creek, there are III. and IV. class lands. These lands are inconvenience or not
useful for agriculture. Mostly fertilized lands are on open and close greenhouse,
citrus fruits and cotton production. Generally, animal manure is used. In addition,
agricultural lime and powder sulfur are applied to improve the soil quality.
42
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 doesn’t 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 aren’t 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 doesn’t 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 isn’t illegal
dumping.
43
4.4 Menderes 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 doesn’t have agriculture
area as big as the Menderes.
Figure 4.2 Menderes Sehitoglu Creek
As seem in Figure 4.2, there aren’t too many settling and facilities. It means that
there aren’t any big pollution sources if the wastes are transported out of the basin.
4.5 Pollutant Sources on Menderes Sehitoglu Creek
Mostly important settlements are Menderes, Akcakoy, Derekoy and Develi
around the creek. These may be classified as pollutant sources in this area. Sources
are evaluated according to their facilities.
44
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..) , Acılım 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 aren’t any
industrial facilities. Akcakoy’s 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 doesn’t 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
Menderes. Its population is 1592 capita (DIE, 1997). Its pollution characteristics are
familiar to agricultural wastes. But pollutants reach the stream as diffuse source by
infiltration.
45
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.
4.6 Existence 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)
Figure 4.3 Names of the studied creek
46
Water Flow Data
13 stream’s drain area: 6,10 km2
15 stream’s drain area : 33,5 km2
14 stream’s drain area: 20,4 km2
16 stream’s drain area : 17,7 km2 (IZSU,2000)
According to these data;
Q13 = 0,052 m3/s
Q14 = 0,18 m3/s
Q17 = 3,73 m3/s
Q15 = 0,522 m3/s
Q16 = 0,15 m3/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 dewpoint 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…).
47
Table 4.2 Quality parameters
Quality Parameters
1
2
Fluoride (mg/L)
Total phosphorus(mg/L)
25
26
Magnesium (mg/l)
Bicarbonate (mg/L)
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Biological Oxygen Demand (mg/L)
Phosphate (mg/L)
Phosphate Phosphorous (mg/L)
Ammonia Nitrogen (mg/L)
Nitrite Nitrogen (mg/L)
Nitrate Nitrogen (mg/L)
Suspended Solid (mg/L)
Total Dissolved Solid (mg/L)
Dissolved Oxygen (mg/L)
Chemical Oxygen Demand (mg/L)
Sodium (mg/L)
Potassium (mg/L)
Color (Pt/Co)
Phenol Matter (mg/L)
Temperature (°C)
Emulsified oil and Grease(mg/L)
Oxygen Saturation (%)
Methyl Blue Active (mg/L)
PH
Conductivity (umhos)
Total Hardness (Fr)
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
Chloride (mg/L)
Organic Matter (mg/L)
Sulfate
Lead (mg/L)
Total Chromium (mg/L)
Chromium (6)(mg/L)
Zinc (mg/L)
Mercury (mg/L)
Cadmium(mg/L)
Copper (mg/L)
Boron (mg/L)
Iron (mg/L)
Nickel (mg/L)
Barium (mg/L)
Aluminum (mg/L)
Arsenic (mg/L)
Manganese (mg/L)
Total Coliform (100ml)EMS
E. Coli 37C / ml'de
Fecal Coliform (100ml) EMS
Fecal Streptococcus 100ml
24
Calcium (mg/l)
There is two stations for sampling on the studied creek (Figure 4.3). Some
pollutant 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 isn’t a water quality stations on the headwater. So, there isn’t any clear
information about headwater quality. But it is known that there aren’t industrial
facilities and settling places. Headwater shouldn’t be polluted from any sources. If it
is assumed headwater quality as clean water, its characteristics can be accepted as I.
class water in Water Pollution Control Regulation. So, its quality variables take place
in Table 4.3
48
Table 4.3 Headwater quality [Uslu & Turkman, 1987]
Parameter
PH
Conductivity
Salt
Total Hardness
Calcium
Magnesium
Bicarbonate
Chloride
Organic Matter
Ammonia
Free Chlorine
Sulfate
Sulphur
Lead
Total Chromium
Chromium (+6)
Zinc
Mercury
Cadmium
Copper
Boron
Iron
Nickel
Barium
Unit
umhos
S%O
Fr
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
7,5
400
0
27
84
17
270
25
0,8
yok
0
18
0
0,003
0,001
0,001
0,09
0
0
0,003
0,17
0,1
0,005
0,07
Parameter
Fluoride
Total Phosphorus
Total Cyanide
Biochemical Oxygen Demand
Phosphate
Phosphate Phosphorous
Ammonium Nitrogen
Nitrite Nitrogen
Nitrate Nitrogen
Suspended Solid
Total Dissolved Solid
Dissolved Oxygen
Chemical Oxygen Demand
Sodium
Potassium
Selenium
Color
Phenol
Temperature
Emulsion Oil and Grease
Oxygen Saturation
Methyl Blue Active Matter
Chlorine
Total Coliform
Unit
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
Pt/Co
mg/L
°C
mg/L
%
mg/L
ppm
100 ml / EMS
0,44
0
0
4
0,04
0,02
0,02
0,002
2,5
48
50
9
10
12
1
5
18
0,03
80
0,03
0
100,000
49
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 this way. For this
purpose, these methods will be applied on Menderes Sehitoglu Creek. At the end of
the examination, needed variables will be chosen.
5.2 Graphics of Water Quality Variables
One of the important water sources in Izmir is Tahtali Dam. There are number of
tributaries in the basin. One of these tributaries is Menderes Sehitoglu Creek. In this
part, the water quality characteristics of Menderes Sehitoglu Creek are examined.
49
50
The data from Menderes Sehitoglu Bridge Station are taken into consideration. The
quality variable, which is obtained in the station, is analyzed in this part.
Each variable from Menderes Sehitoglu Bridge Station were plotted into graphics.
These values include period of December-1996 and April-2000. Some variables had
been observed as once or twice a month. Some of the variables were sampled only
for a short time. All the graphics of the variables are presented in Appendix-B.
According to graphical presentation, all variables show different trend. pH generally
disperse around the mean value in its graphics. Chlorine increase rapidly in October97 and June and July-98. After that, there is not a big deviation along the April-2000.
Boron, florid, BOD concentration are not examined continuously. BOD values
increase as linear between May-98 and August-98. BOD generally shows constant
values. Nitrite nitrogenous increase until February-97 and then decrease until May98. After May -98, its value reaches 0 mg/l. Dissolved oxygen doesn’t change
suddenly between November-97 and 98. But, it goes up until March-99. Sodium
values show a linear increase between May-98 and August-98. There isn’t a big
oscillation among sodium values.
5.3 Statistical Analysis of Water Quality Variables
One of the mostly used methods is statistical analysis for selection of needed data.
Some values are obtained by using statistical analysis. These values are mean,
maximum, minimum, variance, standard deviation, mod, and median. Tendency
analysis of water quality variables is used to put forward an idea on past and future
behaviors of the water quality. Descriptive statistics are determined in this part.
Variables show different values at different time. Minimum and maximum values are
calculated. A symbolized value for each parameter is computed as called mean.
Other statistic values are mod, median and standard deviation. Mod is mostly used
value. Median is a medium value which is higher than 50 % of variables and lower
than 50 % of variables. Standard deviation is calculated to show amount of data set
of variables which swerves from mean. These values of Menderes Sehitoglu Creek
place in Table 5.1
51
Parameters
Number of samples
Minimum
Maximum
Mean
Mod
Median
Std. Deviation
Variance
Table 5.1 Statistic values of variables at Menderes Sehitoglu Creek
Fluoride (mg/L)
39
0,100
0,720
0,401
0,390
0,400
0,011
0,012
Total phosphorus(mg/L)
Biological Oxygen Demand
(mg/L)
28
0,000
0,180
0,024
0,000
0,000
0,047
0,002
39
0,000
12,000
2,770
1,000
1,000
3,000
9,024
Phosphate (mg/L)
11
0,000
0,190
0,033
0,000
0,000
0,064
0,004
Phosphate Phosphorous (mg/L)
11
0,000
0,065
0,011
0,000
0,000
0,022
0,000
Ammonium Nitrogen (mg/L)
38
0,000
1,000
0,063
0,000
0,000
0,205
0,042
Nitrite Nitrogen (mg/L)
41
0,000
0,350
0,037
0,000
0,000
0,075
0,006
Nitrate Nitrogen (mg/L)
40
0,000
15,600
4,148
0,000
3,275
3,820
14,595
Suspended Solid (mg/L)
11
0,000
18,000
5,200
2,000
2,400
5,319
28,296
Total Dis. Solid (mg/L)
31
177,000
889,000
385,581
365,000
353,000
151,731
23022,385
Dissolved Oxygen (mg/L)
Chemical Oxygen Demand
(mg/L)
31
4,400
15,700
7,319
5,600
6,400
2,637
6,951
42
1,500
43,000
8,043
4,000
6,400
7,298
53,257
Sodium (mg/L)
42
11,000
155,000
31,626
17,000
21,750
31,903
1017,818
Potassium (mg/L)
5
3,600
3,900
3,720
3,600
3,700
0,130
0,017
Color (Pt/Co)
31
0,000
50,000
12,871
10,000
10,000
11,584
134,183
Phenol (mg/L)
31
0,000
8,300
0,299
0,000
0,000
1,489
2,218
Temperature (°C)
Emulsified Oil and Grease
(mg/L)
22
4,000
26,000
13,586
10,000
11,750
5,949
35,384
30
0,000
27,000
2,193
0,200
0,800
4,995
24,948
Oxygen Saturation (%)
28
54,000
174,000
86,846
60,000
78,000
30,302
918,185
Methyl Blue Active (mg/L)
28
0,000
1,000
0,127
0,100
0,085
0,206
0,042
PH
Conductivity (µmhos)
42
7,200
8,400
8,005
8,000
8,000
0,281
0,079
11
380,000
600,000
487,727
500,000
490,000
56,981
3246,818
Total Hardness (Fr)
11
21,000
30,000
26,727
28,000
28,000
2,867
8,218
Calcium (mg/l)
11
48,000
80,000
70,182
80,000
72,000
10,226
104,564
Magnesium (mg/l)
11
16,000
26,000
22,091
22,000
22,000
2,948
8,691
Bicarbonate (mg/L)
11
210,000
307,000
270,091
268,000
268,000
30,399
924,091
Chloride (mg/L)
42
18,000
370,000
56,619
30,000
35,000
72,428
5245,754
Organic Matter (mg/L)
11
1,000
3,000
1,946
1,000
2,000
0,780
0,609
Sulfate
28
7,000
92,000
36,071
40,000
30,500
21,333
455,106
0,000
Lead (mg/L)
10
0,000
0,030
0,006
0,005
0,004
0,009
Total Chromium (mg/L)
13
0,000
0,008
0,003
0,001
0,002
0,003
0,000
Chromium (6)(mg/L)
4
0,001
0,006
0,002
0,001
0,001
0,003
0,000
Zinc (mg/L)
13
0,009
0,170
0,074
0,010
0,061
0,056
0,003
Mercury (mg/L)
2
0,003
0,009
0,006
0,003
0,006
0,004
0,000
Cadmium(mg/L)
2
0,001
0,002
0,002
0,001
0,002
0,000
0,000
Copper (mg/L)
14
0,002
0,015
0,005
0,002
0,004
0,004
0,000
Boron (mg/L)
38
0,080
1,070
0,282
0,160
0,235
0,192
0,037
Iron (mg/L)
14
0,020
4,800
0,558
0,050
0,220
1,245
1,550
Nickel (mg/L)
10
0,001
0,016
0,006
0,001
0,005
0,005
0,000
Barium (mg/L)
14
0,025
0,470
0,071
0,040
0,040
0,115
0,013
Aluminum (mg/L)
12
0,040
7,400
0,827
0,060
0,098
2,092
4,375
Arsenic (mg/L)
4
0,001
0,003
0,002
0,001
0,002
0,000
0,000
Manganese (mg/L)
14
0,005
0,240
0,044
0,017
0,024
0,060
0,004
52
Each variable had been analyzed min 11 and max 42 times according to this table.
Continuously analyzed parameters are pH, chlorine, sulfate, boron, fluoride, total
phosphorus, biological oxygen demand, ammonium nitrogen, total dissolved solid
matter, dissolved oxygen, chemical oxygen demand, sodium, color, phenol,
emulsified oil and grease, nitrite and nitrate nitrogen.
5.4 Evaluation of Statistical Analysis
These statistical analyses give an idea about water quality and its tendency. So,
these values are examined to estimate water quality in this part. pH values change
between 7,2 and 8,4. Mean, mod and median value are 8. It means that there is not a
significant change among the values. pH value is generally around the mean.
Chlorine concentrations are between 18 – 370 mg/l. mostly repeated value is 30 mg/l,
median is 35 mg/l. Mean of the values is 56 mg/l. It means that there is some high
concentrations. Total phosphorus values are between 0 – 0,18 mg/l. Mod and median
values are 0 mg/l. So, this variable value is generally 0 mg/l, but sometimes its value
increases up to 0,18 mg/l. It means there may be some pollution sources. Biological
oxygen demand values changes between 0 – 12 mg/l. Mod and median values are 1
mg/l. It means that 50 % of values are under the 1 mg/l. Other values are between 1 –
12 mg/l. So that, there is some unexpected increase. Ammonium nitrogen is analyzed
37 times. Mod and median values are 0 mg/l. Generally 0 mg/l is experienced but
sometimes value increases up to 1 mg/l. This value forms that there is sudden
concentration increase. Sodium is analyzed 42 times and its values change between
11 – 155 mg/l. Mostly repeated value is 17 mg/l and median is 21.75 mg/l. Mean
value is 31 mg/l. It means that there is some unexpected increase. Emulsified oil and
grease values change between 0 – 27 mg/l. Mod and median values are 0,2 mg/l and
0,8 mg/l. Namely, 27 mg/l is observed as a result of sudden increase. Sulfate values
are between 7 – 92 mg/l. Median value is 30 mg/l, mean value is 36 mg/l. Maximum
value is found 92 mg/l. It means that sulfate concentration displays high values.
Boron is analyzed 38 times. Results of analysis change between 0,08 – 1,07 mg/l.
Mean value is 0,282 mg/l and median is 0,253 mg/l. Mostly repeated value is 0,16
mg/l. Sudden concentration increase is observed, because maximum concentration is
53
1,07 mg/l. Nitrite nitrogen is analyzed 41 times. Values are between 0 – 0,35 mg/l.
Mod and median values are 0 mg/l and mean value is 0,037 mg/l. This condition
shows that there is sudden concentration increase. Nitrate nitrogen values change
between 0 – 15,6 mg/l. Mostly repeated value is 0 mg/l, and median is 3 mg/l. If it is
considered 4 mg/l as mean value, there is some sudden increase to reach maximum
concentration (15,6 mg/l). Results of methyl blue active matter values change
between 0 – 1 mg/l. Mean value is 0,1 mg/l, median value is 0 mg/l. There is sudden
concentration increase because maximum value is 1 mg/l. 28 Analysis is made for
oxygen saturation. Minimum and maximum values are 54% and 174%. Mod value is
65%, and median value is 78%. Mean value is 86%. There must be sudden
concentration increase to reach maximum value. Phenol is analyzed 31 times. Values
are between 0 – 8,3 mg/l. Mod and median values are 0 mg/l. Mean value is 0,29
mg/l. There must be high concentration because of maximum value (8,3 mg/l). Color
is analyzed 31 times. Minimum value is 0 pt/co and maximum value is 50 pt/co.
Mostly repeated value is 10 pt/co and median value is 10 pt/co. Mean value is 12
pt/co. There must be sudden increase because of maximum value. Chemical oxygen
demand values changed between 1,5 – 43 mg/l. Mod value is 4 mg/l and median
value is 6 mg/l. There must be high concentrations because mean value is 8 mg/l.
Dissolved oxygen is analyzed 31 times. Results of the analysis are between 4 – 15
mg/l. Median value is 6 mg/l and means value is 7 mg/l. This condition seems that
there is a sudden concentration increase at a specific point. Total dissolved solid
matter values change between 177 – 889 mg/l. Mean value is 385 mg/l. Mod value is
365 mg/l and median value is 353 mg/l. There is some sudden increase because
maximum value is 889 mg/l.
When these results are considered, it can be appeared which parameters should be
modeled. According to this method, most important parameters are biological
oxygen demand, phosphorous and nitrogen. Their values are higher than boundary
levels. Therefore Qual2K should be run for BOD, P and N variables.
54
CHAPTER SIX
APPLICATION OF THE MODEL TO THE STUDY AREA
6.1 General
Qual2k model is applied to the case of Menderes Sehitoglu Creek that is a
tributary of Tahtali Creek. Menderes Sehitoglu Creek is a part of Tahtali river basin
(Figure-4.1) and the water quality is modeled in the presented study. The required
data for model application are obtained from IZSU and DSI. Here, it should be noted
that, the data are not enough to run the model satisfactorily, therefore, the missing
data are fulfilled by assumptions. As first step in model application is to divide the
studied river system into reaches. Considering the hydraulic characteristics, crosssectional areas, location of the stations and basic point sources, the river is divided
into six reaches as indicated on Figure 6.1
Figure 6.1 Reaches of the studied stream
55
The other step in the application is preparation and entering of the relevant data
using in Qual2k. After entering the data, some scenarios are created as convenient to
the basin. In this study, scenarios are concerned with point sources characteristics.
Main point source arises from Menderes and Develi domestic wastewater. So, six
scenarios are formed according to domestic wastewater characteristics. These will be
explained in point sources sheet part.
In fact, there isn’t only domestic wastewater. There is industrial wastewater and
slaughterhouse wastewater and solid wastes as explained in Chapter-4. According to
information about the basin obtained from the IZSU and relevant municipalities, a
great amount of these wastes are collected and transported out of the basin. So that,
they are not taken into consideration.
As stated previous chapter, the model is run for some the water quality variables;
Biological oxygen demand(BOD), Dissolved oxygen (DO), Temperature (T),
Nitrogen (N), Phosphorus (P). The model is run with average of spring months
values of runoff and water quality. In summer most of the creeks in the basin are
withering. Most suitable runoff values are obtained in spring months. Now each step
and sheet of the model will be explained following parts with some assumptions and
calculations.
6.2 Headwater Data
Some information about headwater was placed in chapter 4. Now, this
information is summarized. There isn’t a water quality stations on the headwater. So,
there isn’t any clear information about headwater quality. But it is known that there
aren’t industrial facilities and settling places. Headwater shouldn’t be polluted from
any sources. It is assumed that headwater is as clean water. Its values are showed on
Figure 6.2
56
Figure 6.2 Headwater quality inputs
This water quality values are assumed as it is I. class water quality and its values
are stationary for all day. Because this system behaviors as a steady state condition.
There are headwater and downstream boundary water quality concentrations data. If
the downstream boundary has an effect, it can be used. But in this study it didn’t
used. The headwater flow rate had been calculated in Chapter 4. It was used here as
m3/s.
Some calculations were made for missing data with using some assumptions.
They were for inorganic solids, CBODslow, CBODfast, dissolved organic nitrogenous,
dissolved organic phosphorus, inorganic phosphorus.
Total solids are total of the filterable and suspended solids. Filterable solids are
total of the colloidal and dissolved solids. Suspended solids are total of the settleable
and nonsettleabe solids. They are summarized on Figure 6.3
57
Total solids (100)
Suspended solids (30)
Settleable(22)
Nonsettleable(8)
Org.
Inorg.
Org.
(17)
(5)
(6)
Inorg.
(2)
Filterable solids (70)
Colloidal(7)
Dissolved (63)
Org.
Inorg.
Org.
Inorg.
(6)
(1)
(22)
(35)
Figure 6.3 Solids form in water
As seem on Figure-6.3, some values are given for each form of solids. By using
this value, their percentage value can be found. This figure is referred from
Meddcalf&Eddy as medium-strenght water. Following information are obtained
from these percentages.
Dissolved solids = 50 mg/l
(assume as clean water)
Total solids
= 80 mg/l
(computed from Figure-6.3)
Inorganic solids = 40 mg/l
(computed from Figure-6.3)
BOD is amount of oxygen consumed by bacteria from the decomposition of
organic matter (Sawyer et al., 1978). There are two stages of decomposition in the
BOD. These are a carbonaceous stage and a nitrogenous stage. The carbonaceous
stage, or first stage, represents that portion of oxygen demand involved in the
conversion of organic carbon to carbon dioxide. The nitrogenous stage, or second
stage, represents a combined carbonaceous plus nitrogenous demand, when organic
nitrogen, ammonia, and nitrite are converted to nitrate.
NBOD is 40 % of ultimate BOD. So CBOD is 60 % of ultimate BOD (Eliasson J.,
2003). It is assumed that CBODslow is equal to CBODfast.
BOD value of headwater is 4 mg/l. CBOD = 4 x 0,60 = 2,4 mg/l
CBODslow = CBODfast = 1,2 mg/l
58
Dissolved organic nitrogenous (DON) is 70 % of total nitrogen (Kausha et al.
1979). Total nitrogen is overall of NO3-N, NO2-N and NH4-N. These values from
headwater quality are;
NO3-N = 2,5 mg/l,
NO2-N = 0,002 mg/l,
NH4-N = 0,02 mg/l,
Total-N = 2,522 mg/l = 2522 µg/l
DON = 2522 x 0,7 = 1770 µg/l
Dissolved organic phosphorus (DOP) is 35 % of total phosphorus. Inorganic
phosphorus is 65 % of the total phosphorus (Kausha et al. 1979). Total phosphorus is
equal with phosphate phosphorus (PO4-P).
PO4-P = 0,02 mg/l = 20 µg/l (see table-5)
Total-P = 20 µg/l DOP = 20 x 0,35 = 7 µg/l
Inorganic-P = 20 x 0,65 = 13
µg/l
Alkalinity means a characteristic of neutralizing acids. Types of alkalinity are
HCO3-, CO3= and OH- alkalinities. Alkalinity type changes according to ph. When ph
is between 4.5 and 8.3, there are HCO3- and CO3= alkalinities. Ph is 8,2 and HCO3- is
270 mg/l in headwater quality. By using this information HCO3- alkalinity can be
found.
[HCO3-] = 270 mg/l = 270 / 2 = 60 meq/l
1meq
60 mg
?
270 mg
[HCO3-] = 4,5 meq/l
(CaCO3 = 50 mg/meq)
[HCO3-] = 4,5 x 50 = 225 mg CaCO3/l
Suspended particulate organic matter (SPOM) in streams and rivers has been
viewed both as a central component of ecosystem dynamics and as terrestrial refuse
on its way to the ocean or sea. If SPOM exchanges rapidly and extensively with the
59
benthic POM (BPOM), the POM migrates downstream, on the average, a few meters
per day (increasing downstream) and a particle may reside in the basin more than a
decade. POM from first- and second-order streams may contribute 15-20% of the
carbon metabolism of third order streams, with little export of labile POM. If,
however, the SPOM/BPOM exchange is limited, the mobile POM may move
downstream tens of meters per day and leave the basin within a year. Headwater
exports account for less metabolism in mid-order reaches but more labile SPOM is
exported from the basin (Kausha et al. 1979)..
Detritus means particulate organic matter. In headwater quality, detritus was
assumed as a part of suspended solids. Approximately, 20% of suspended solids
may be accepted as detritus. In all computation, the detritus was assumed 20% of
suspended solids.
6.3 Reach Data
Stream was divided into segments. These segments showed some certain points.
Reach data screen is showed on Figure 6.4
Figure 6.4 Reach data
There is 6 reaches. In the model, some information about each reach is needed.
They are reach length, elevation, latitude and longitude…etc. Elevation of this area is
about 450m. Channel slope of the creek is very low. So that, each reach elevation
value is written approximately. Latitude and longitude values were estimated from
Izmir values. Izmir is between 26 – 28 east longitude and 37 – 38 north latitude.
According to this information and map knowledge, each reach values are written.(as
seem on Figure 6.5)
60
Figure 6.5 Elevation, latitude and longitude data
For hydraulic model, there are two options. These are rating curves and manning
formula. Information about them was explained in Chapter III. One of them must be
chosen and its data must be filled. In this study, manning formula was selected. Its
needed data are bottom width, channel slope, side slope and manning-n. Bottom
width and channel slope data are placed in IZSU references. Side slope is zero
because channel cross-sectional shape is rectangle. Manning-n data is determined
according to channel condition (man-made, natural, sandy...etc.). This channel is
natural. So, most suitable value for manning-n is 0,04. Other values for manning-n
can be found in manual documentation of Qual2K. Hydraulic data are showed on
Figure 6.6
Figure 6.6 Hydraulic model data
Prescribed dispersion, prescribed reaeration, bottom algae, bottom SOD,
prescribed SOD, prescribed CH4 flux, NH4 flux and inorganic-P flux are assumed as
61
same as original model application. There is not any information about weir height,
so it is assumed as 0 m.
Figure 6.7 Continued reach data
6.4 Meteorology and shading Data
In this worksheet, segmentation and their distance are placed automatically. Our
data are not change reach to reach. So we use this information for all reach and hour.
These data are related to air temperature, dew-point temperature, wind speed,
shading and cloud cover information. Air temperature and wind speed data around
the creek was supplied from meteorology centre.
Air temperature is 20 0C around the Menderes Sehitoglu Creek. Wind speed is 5
m/s. Dew-point temperature is calculated by using air temperature and relative
humidity. This calculation takes part in Appendix-D. Dew-point temperature was
calculated as 14,5 0C. Cloud cover and shading were assumed as same as original
model. This information is on Figure 6.8
62
Figure 6.8 Meteorology and shading data
6.5 Rates, Light and Heat Data
Rates worksheet is used to enter the model’s rate parameters. There is model
assumes a fixed stoichiometry of plant and detrital matter. Recommended values for
these parameters take place on Figure 6.9. Settling velocity of inorganic suspended
solids is assumed as 1 m/d.
Figure 6.9 Rates data
For oxygen, there are some formula options and some assumptions accepted by
the model. This information had been explained in Chapter III. These are only
showed on Figure 6.10 in this chapter.
Figure 6.10 Continued rates data
63
Slow CBOD, fast CBOD, organic-N, ammonium, nitrate and organic-P were
suggested by the model. At the same time, phytoplankton, bottom algae, detritus,
pathogens and ph rates are given in the original model. Phytoplankton and bottom
algae light model options take part in the rates worksheet. These options were
explained in Chapter three. One of the options can be selected. In this study, model
choices were accepted. Other rates data are on Figure 6.11.
Figure 6.11 Continued rates data
64
Light and heat data worksheet is used to enter information related the system’
light and heat parameters. This worksheet information was taken part in Chapter III.
There are some assumptions and light and heat models suggested by the Qual2K. In
this study, model suggestions were used. In solar short wave radiation model, there
are two options for atmospheric attenuation model (Bras and Ryan-Stolzenbach).
Atmospheric turbidity coefficient is used if the Bras Model is used. We selected Bras
model and atmospheric turbidity was used as 2 (clear). Atmospheric transmission
coefficient is used if Ryan is selected. Atmospheric longwave model has some
options. Its pull down menu contains Brutsaert, Brunt and Koberg models. Brunt is
selected
in
this
study.
Wind
speed
function
for
evaporation
and
air
convection/conduction has three options (Brady- Graves-Gayer, the Adams 1, the
Adams 2 models). Brady-Graves-Gayer model is default here. These data are given
on Figure 6.12
Figure 6.12 Light and heat data
6.6 Point Sources Data
There is information about system’s point sources in this part. These point sources
were examined and their characteristics were determined. Firstly, point sources are
explained. These point sources are given on Figure 6.13
65
1
Basin main Creek (17)
Oglananası
16 number Creek
Menderes ww.
Headwater
4
2
(14)
3
1
5
Develi
ww.
13 and 15 number Creek
Derekoy
Akcakoy
Figure 6.13 Point sources
As seem on Figure-6.13, there are 5 main point sources. Each point source
characteristics is examined in this part. On the studied stream, the quantity of water
increases with contribution of the tributary on 15st kilometer. This tributary is main
stream of the basin, so its quantity is very high. It is called as 3. point source. Other
tributary contributions are on 14st and 15,5st kilometers. They are called as 2. and 4.
point sources. On 13,5st and 15,5st kilometers, other point loads are contributed to the
stream. These are domestic discharges. They are called as 1. and 5. point sources. In
this study, domestic wastewaters are taken into consideration. Domestic wastewater
sources are Menderes and Develi village located in the basin. The amount of
wastewater per capita per day is taken as 200 L/ca.day. According to the population
of the residential areas, the domestic wastewater discharges are calculated as in Table
6.1
Table 6.1 Domestic wastewater discharges in the studied basin
Name
Population (ca)
Q (m3/d)
Q (m3/s)
Menderes
15720
4838
0,056
Develi
1592
346
0,004
Other important element of this part is that different scenarios are evaluated.
These scenarios are created from domestic wastewater characteristics. These are;
66
1. No treatment
2. 90% BOD treatment
3. 90% BOD and 90% N treatment
4. 90% BOD and 90% P treatment
5. 90% BOD and 90% N- 90% P treatment
6. No domestic wastewater (it is assume that wastewater is transported out of
the basin by vehicle)
These scenarios results is given in Chapter seven.
Simulation of the river includes point sources and diffuse sources. This simulation
can be showed with segmentation on Figure 6.14.
Figure 6.14 System segmentation and location of pollution sources
67
Now, each point source is examined and their characteristics and effect of the
stream is revealed.
1. Point Source
1 point shows domestic wastewater characteristics. There are industrial
facilities. But their wastes are taken away, so industrial effects are neglected.
Menderes population is 15720 capita. There is Gumus Mestanli Houses. Its
population is also 8400 capita. We assume that each person forms 200L wastewater
per day. So total flow of domestic wastewater is 0,056 m3/s. It was assumed that all
domestic wastewater was discharged into the creek. Domestic wastewater
characteristics are given in Appendix-E.
Domestic wastewater amount 0,056 m3/s.
The quality values of the modeled variables relevant to domestic wastewater are
used as the literature values as given in Table 6.2
Table 6.2 Quality values of the modeled variables
Parameter
Value
(Menderes)
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
25
BOD (mg/l)
220
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
According to 1. scenario, 1.point source is assumed that it is raw domestic
wastewater. There is no treatment process for wastewater. 1.Point source
characteristic is given in Table 6.3
68
Table 6.3 Quality values for the 1. scenario (Menderes)
Parameter
Value
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
25
BOD (mg/l)
220
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
These values were entered the model point source sheet. They are used for
assuming the worst scenario result. Domestic wastewater aren’t treated and
discharged directly. Variables of the 1. scenario take place on Figure 6.15.
69
Figure 6.15 Point source data according to 1. scenario
According to 2. scenario, 1.point source is assumed that it is treated as %90
efficiency for BOD. There is a treatment process only for biological oxygen demand.
1.Point source characteristic is given in Table 6.4
Parameter
Value
0
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
25
BOD (mg/l)
22
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
assuming 90% BOD removal. Domestic wastewater are treated as biological and
discharged into the stream. Variables of the 2. scenario take place on Figure 6.16.
70
efficiency BOD and %90 efficiency N. There is treatment processes for BOD and N.
Parameter
Value
0
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
2,5
BOD (mg/l)
22
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
1,5
Alk. (mgCaCO3/l)
100
pH
7,3
assuming 90% BOD and 90% N removal. Domestic wastewater are treated for
eliminating of BOD and N variables and discharged into the stream. Variables of the
3. scenario take place on Figure 6.17.
71
efficiency BOD and %90 efficiency P. There is treatment processes for BOD and P.
Parameter
Value
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
25
BOD (mg/l)
22
Org-P (mg/l)
0,3
ISS (mg/l)
55
Inorg-P (mg/l)
0,5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
72
assuming 90% BOD and 90% P removal. Domestic wastewater are treated for
eliminating of BOD and P variables and discharged into the stream. Variables of the
4. scenario take place on Figure 6.18.
efficiency BOD and %90 efficiency N and P. There is treatment processes for BOD,
N and P. 1.Point source characteristic is given in Table 6.7
73
Parameter
Value
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
2,5
BOD (mg/l)
22
Org-P (mg/l)
0,3
ISS (mg/l)
55
Inorg-P (mg/l)
0,5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
1,5
Alk. (mgCaCO3/l)
100
pH
7,3
assuming 90% BOD, 90% P and 90% N removal. Domestic wastewater are treated
for eliminating of BOD, P and N variables and discharged into the stream. Variables
of the 5. scenario take place on Figure 6.19.
74
According to 6. scenario, it is assumed that domestic wastewater are collected by
piping or vehicles and transported to out of the basin. So there is not an effect of the
wastewater on stream quality. 1.Point source characteristic is given in Table 6.8
Parameter
Value
0
Parameter
Value
Temperature ( C)
0
NH4-N (mg/l)
0
BOD (mg/l)
0
Org-P (mg/l)
0
ISS (mg/l)
0
Inorg-P (mg/l)
0
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
0
Org-N (mg/l)
0
Alk. (mgCaCO3/l)
0
pH
0
assuming the best scenario result. Domestic wastewater is not discharged into the
stream. So, this scenario is the most optimistic approach. Variables of the 6. scenario
take place on Figure 6.20.
75
2. Point Source
In fact, 2 point source is another tributary in the basin as same as 3 and 4 point
sources. The factors of affected the water quality in 2 point are the settlements,
agriculture area and nature content of the creek. Along this creek, there is some
settlements and agricultural area. Their effects are not very big. Because settlements
are village and their population is very low. But, they are taken into consideration.
Village population is 500 capita. If it is assumed that each person forms 200L
wastewater per day, domestic flow rate is 0,0016 m3/s. Domestic wastewater
characteristics are used as the literature values (see Table 6.2)
Agriculture area is 3500 da. Mostly wet agriculture is applied. This is another
pollution source affected to 2 point source. If it is assumed that 1 m3 water is needed
for 1 da per day, needed irrigation water is 0,04 m3/s. This water is used by the plant
for growing and a large amount of water is reach to underground water level. So, a
little part of water is reach to the creek by run-off. This amount is assumed as % 50
of total amount irrigation water.
Agricultural flow rate 0,02 m3/s
Run-off water characteristics from agricultural area were found in Water
Pollution Control Book. (see Table 6.9)
Table 6.9 Agricultural run-off water characteristics (Uslu & Turkman, 1987)
Parameter
Temperature (0C)
BOD (mg/l)
Value
Parameter
Value
-
NH4-N (mg/l)
1,629
100
Org-P (mg/l)
0,35
0,65
ISS (mg/l)
0
Inorg-P (mg/l)
NO3+NO2-N(mg/l)
-
Cond. (umhos/cm)
-
3,801
Alk. (mgCaCO3/l)
-
Org-N (mg/l)
pH
-
76
Another factor is nature content of the river. Whole river include some
parameters. According to this general information, natural river content is given in
Table 6.10
Creek flow rate 0,339 m3/s
Table 6.10 Natural river content (Uslu & Turkman, 1987)
Parameter
Value
Parameter
Value
Temperature (0C)
-
NH4-N (mg/l)
0,2
BOD (mg/l)
2
Org-P (mg/l)
0,0105
ISS (mg/l)
25
Inorg-P (mg/l)
0,0195
NO3+NO2-N(mg/l)
Org-N (mg/l)
pH
-
Cond. (umhos/cm)
-
1,891
Alk. (mgCaCO3/l)
225
7,3
Creek flow rate is 0,34 m3/s. All of the pollutants mixed with this creek
water. And, creek water quality changes by the pollutant sources. 2.Point source
characteristics can be determined by using this pollutant and natural water values.
(see Table 6.11)
Table 6.11 Second point source values
Parameter
Value
0
Parameter
Value
Temperature ( C)
18
NH4-N (mg/l)
1
BOD (mg/l)
3
Org-P (mg/l)
0,1
ISS (mg/l)
25
Inorg-P (mg/l)
0,2
NO3+NO2-N(mg/l)
2,5
Cond. (umhos/cm)
600
3
Alk. (mgCaCO3/l)
200
Org-N (mg/l)
pH
7,5
77
3. Point Source
3 point source is another tributary comes from upper basin region. There are a lot
of industrial, domestic and agricultural waste sources. There are some water quality
stations. One of the stations is selected that is much more suitable for these creek
characteristics and nearest to the studied basin. This station gives us 3. point source
characteristics. These needed parameters are;
Its flow rate is 3,73 m3/s. According to other basin station, 3. point source values
must be as in Table 6.12.
Table 6.12 Third point source values
Parameter
Value
0
Parameter
Value
Temperature ( C)
18
NH4-N (mg/l)
0,13
BOD (mg/l)
3,3
Org-P (mg/l)
0,09
ISS (mg/l)
37
Inorg-P (mg/l)
0,18
NO3+NO2-N(mg/l)
5,16
Cond. (umhos/cm)
468
Org-N (mg/l)
3,7
Alk. (mgCaCO3/l)
190
pH
7,6
DO (mg/l)
9
4. Point Source
This is another creek as point source. There is Oglananasi near the creek. Its
population is 1877 capita. This settling place has got a new infrastructure system, so
their wastewater is transported out of the settling regularly. On the other hand, there
are agricultural areas. This area is 24000 da. Mostly wet agriculture is applied. So
this area may be affected creek water quality. But, flows of pollution sources are
least than creek water flow. So, water quality of the creek is assumed as natural
surface water quality. This creek flow rate is 0,15 m3/s. Its values are given in Table
6.13.
78
Table 6.13 Fourth point source values
Parameter
Value
Parameter
Value
Temperature (0C)
-
NH4-N (mg/l)
1
BOD (mg/l)
3
Org-P (mg/l)
0,1
Inorg-P (mg/l)
0,2
ISS (mg/l)
2,5
NO3+NO2-N(mg/l)
-
Cond. (umhos/cm)
600
Org-N (mg/l)
3
Alk. (mgCaCO3/l)
200
pH
7,5
DO (mg/l)
8
5. Point Source
Develi is another settlement near the creek. There is domestic ww. as point
source. Develi capita is 1592. So that, its flow rate is very low. This domestic waste
water is as same as 1. point source. Its scenarios are current for 5. point source.
Domestic wastewater amount 0,004 m3/s.
The quality values of the modeled variables relevant to domestic wastewater are
used as the literature values as given in Table 6.14
Table 6.14 Quality values of the modeled variables (Develi)
Parameter
Value
0
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
25
BOD (mg/l)
220
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
According to 1. scenario, 1.point source is assumed that it is raw domestic
wastewater. There is no treatment process for wastewater. 1.Point source
characteristic is given in Table 6.15
79
Table 6.15 Quality values for the 1. scenario
Parameter
Value
(Develi)
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
25
BOD (mg/l)
220
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
efficiency for BOD. There is a treatment process only for biological oxygen demand.
Table 6.16 Quality values for the 2. scenario (Develi)
Parameter
Value
0
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
25
BOD (mg/l)
22
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
Parameter
Value
0
(Develi)
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
2,5
BOD (mg/l)
22
Org-P (mg/l)
3
ISS (mg/l)
55
Inorg-P (mg/l)
5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
1,5
Alk. (mgCaCO3/l)
100
pH
7,3
80
efficiency BOD and %90 efficiency N. There is treatment processes for BOD and N.
efficiency BOD and %90 efficiency P. There is treatment processes for BOD and P.
Parameter
Value
(Develi)
Parameter
Value
Temperature (0C)
20
NH4-N (mg/l)
25
BOD (mg/l)
22
Org-P (mg/l)
0,3
ISS (mg/l)
55
Inorg-P (mg/l)
0,5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
15
Alk. (mgCaCO3/l)
100
pH
7,3
efficiency BOD and %90 efficiency N and P. There is treatment processes for BOD,
N and P. 1.Point source characteristic is given in Table 6.19
Parameter
Value
0
Parameter
Value
Temperature ( C)
20
NH4-N (mg/l)
2,5
BOD (mg/l)
22
Org-P (mg/l)
0,3
ISS (mg/l)
55
Inorg-P (mg/l)
0,5
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
10000
Org-N (mg/l)
1,5
Alk. (mgCaCO3/l)
100
pH
7,3
According to 6. scenario, it is assumed that domestic wastewater are collected by
piping or vehicles and transported to out of the basin. So there is not an effect of the
81
wastewater on stream quality. 1.Point source characteristic is given in Table 6.20
Parameter
Value
Parameter
Value
Temperature (0C)
0
NH4-N (mg/l)
0
BOD (mg/l)
0
Org-P (mg/l)
0
ISS (mg/l)
0
Inorg-P (mg/l)
0
NO3+NO2-N(mg/l)
0
Cond. (umhos/cm)
0
Org-N (mg/l)
0
Alk. (mgCaCO3/l)
0
pH
0
After this examination, related data can be applied to the model. If there is
any abstraction, this data can be enter the model. In this study, there isn’t any
abstraction information.
6.7 Diffuse Sources Data
This worksheet is used to enter information related the system’s non-point
sources. If there is any abstraction, this data can be entered the model. In this study,
there isn’t any abstraction information. Non-point pollution sources generally arise
from agricultural areas. Pollutants pass to the soil by irrigation and rain. These
pollutants located in the soil can reach to the surface waters by surface or subsurface
runoff. When the basin is evaluated, there are many agricultural areas in the
protection areas. This means that the areas are very important for the basin
management. But in this study, mostly point sources effects to the stream are
examined. So that, data was collected for this purpose. Much more detailed
researches must be done for examining diffuse source effects. Its data collection
system is more complicated. Nevertheless, there is some data entered to the model
about diffuse sources. These data are obtained from relevant literature values and
some calculation.
82
As seem on Figure 6.14, there is two diffuse sources. They are Menderes and
Develi agriculture areas. Firstly, their flow rate are found and than entered the
constituents of the sources. Most of related quality parameter values were obtained
from Water Pollution Control Book. At the same time, underground water is
assumed as diffuse source. Its values are gotten from literature sources.
Menderes agriculture area is 23110 da. and Develi agriculture area is 7515 da. If it
is assumed that 1 m3 water is needed for irrigation per day, total amount of water is
0,27 m3/s in Menderes and 0,087 m3/s in Develi. This water is used by the plant for
growing and a large amount of water is reach to underground water level. So, other
part of water is reach to the creek by run-off. This amount is assumed as 25% of total
amount irrigation water.
QMenderes = 0,067 m3/s
QDeveli = 0,022 m3/s
Agricultural drainage water characteristics are changed as to kind of plant and
structure of the soil. In this study, these characteristics are taken from general
literature sources. According to Water Pollution Control Book average N and P
values are;
Total-P = 0,7 mg/l
Total-N = 3 mg/l
Water characteristics are same in Menderes and Develi agriculture areas.
Agricultural drainage water has more different N and P content from domestic
sources. According to literature;
Org-N = 53 % of total-N
inorganic-N = 47 % of total-N
Org-P = 71 % of total-P
inorganic-P = 29 % of total-P
NH4-N = 90 % of inorganic-N
83
By using this general information, some data about diffuse sources can be
obtained. Their summary is given in Table 6.21
Table 6.21 Diffuse source characteristics (Menderes and Develi)
Parameter
Value
Org.-N (mg/l)
1,59
Inorg.-N (mg/l)
1,41
NH4-N (mg/l)
1,269
Org.-P (mg/l)
0,497
Inorg.-P (mg/l)
0,203
ISS (mg/l)
0
0
Temperature ( C)
17
According to these information, diffuse source characteristics can be entered to
the model as on Figure 6.21
Figure 6.21 Diffuse sources data
6.8 Temperature Data
Temperature values are obtained from IZSU stations data set. These data take
place in Appendix A. In this study, average of spring months values are used as
temperature values like other variables and hydraulic values. Two points are selected
for using data set. Because, there are two stations in the studied area for sampling.
These entered data supplied from IZSU are proved us which scenario is realistic.
Entered temperature data are showed on Figure 6.22.
84
Figure 6.22 Temperature data
6.9 Water Quality Data
This worksheet is used to enter mean daily values for water quality data. These
data were obtained from IZSU stations. In studied area, there is two stations.
Samples were collected from 14st and 15,5st kilometers. Results of the sample
analysis were evaluated statistical in 5. Chapter. Spring months average values are
used in this part. Headwater quality is considered as other water quality data. So,
there are three point data along the creek. First station is at 3. point source. Second
station is at the 5. point source. These stations data were entered the water quality
data worksheet. These data sheet is given on Figure 6.23.
Figure 6.23 Water quality data
After entering the data in these sheets, resulting graphs are obtained. The variation
of water quality can be observed along the stream. All the peak points on the graph
correspond to the point load contributions which increase the value of variables on
the stream. These output results will be evaluated in next chapter.
85
CHAPTER SEVEN
EVALUATION OF THE RESULTS
7.1 General
At previous Chapter, the model is run for the water quality variables. In this
Chapter, the model results are evaluated for each scenario. Firstly, it is explained
what kind of output results can be obtained from QUAL2K model. Then, it is
emphasized which of them were used in this thesis.
QUAL2K is one of the software which answers to the some project’s problems.
One of them is one dimensional steady state hydraulics. Because, bi-dimensional
ones are needed. In QUAL2K, different flows can be added. They are diurnal heat
budget, diurnal water quality budget and heat and mass inputs. This software is
programmed in the windows macro language. Visual Basic for Applications (VBA)
is used as the graphical interface. The different points analyzed by QUAL2K are;
Model segmentation
Carbonaceous BOD specification (slow and fast CBOD)
Anoxia
Sediment-water interactions
Bottom algae
Light extinction
Ph
Pathogens
There are many interactions between reaches, loads, abstractions, atmosphere and
sediments. The software QUAL2K is above all a temperature model. Therefore, the
software analyzed surface heat flux and sediment water heat transfer. The different
constituents of the model are the conductivity, inorganic suspended solids, dissolved
oxygen, CBOD, nitrogen, phosphorus, phytoplankton, detritus, pathogen, alkalinity,
carbon algae...etc.
85
86
In this thesis, temperature, inorganic suspended solids, dissolved oxygen, CBOD,
nitrogen and phosphorus were modeled. Their values versus the distance were
obtained to see the variation of variables along the river. For the spring months
values, different scenarios were created. For these six different scenarios, the output
graphs were obtained. Along the river, there is some point sources. These point
sources effects are evaluated in this Chapter. It is estimated how the point sources
can change the quality of the stream water.
7.2 Evalution of the Graphs
Model was run according to six scenarios. These scenarios are constituted from
domestic wastewater characteristics. Domestic wastewater sources are in Menderes
and Develi. It can be showed this wastewater effects to the river by using this
scenarios. At the same time, there are diffuse sources. They are generally raised from
agricultural activities. These agricultural area take a wide place around the studied
basin. In this thesis, their effects are not examined by detailed. But, by using some
assumptions agricultural effects are taken into consideration. Other important point
for the graphics, all point loads effects can be seen. Now, each selected parameter is
examined for each scenario.
For the Biological Oxygen Demand, the variation of water quality can be
observed in Figure 7.1 along the main stream. In this model, BOD is examined as
carbonaceous BOD (CBOD), slow and fast BOD. Slow BOD means slowly reacting,
fast BOD means fastly reacting. According to the first scenario, domestic wastewater
are not treated and discharged directly. This is the worst scenario among the other
scenarios. When the graphics are observed, the peak point on the graph corresponds
to the point load contributions which increase the value of CBOD on the main
stream. The CBOD concentrations reduce at the downstream regions of the river as
the river runoff rate increases by combining of the tributaries. Ultimate CBOD
increase up to 25 mg/L for the first scenario.
87
Figure 7.1 CBODu,CBODslow,CBODfast and DO variation along the stream for first scenario.
Along the main stream, DO generally floctuates around the 8 mg/L. It falls below
8 mg/L at the intersection of the tributaries. (see Figure 7.1)
Temperature, inorganic suspended solids and total suspended solids variation are
observed in Figure 7.2 along the main stream. Temperature is around the 180C along
the river. Total suspended solids (TSS) and inorganic suspended solids (ISS) show a
88
decrease as the river flow rates increase. At point loads, TSS rises up to 15 mg/L and
ISS goes up to 8 mg/L for the first scenario.
Figure 7.2 Temperature, ISS and TSS variation along the stream for first scenario
Figure 7.3 show that concentrations of organic nitrogen (No), ammonia nitrogen
(NH4-N), nitrate nitrogen (NO3-N)) and total nitrogen (TN) are high at points of
domestic discharges. Generally they show a decreasing slope to the point loads
(13,5km) from the headwater. Only NH4-N increase for this part because of
89
agricultural irrigation runoff. At point loads, NH4-N and No rise up to 3,5 mg/L and
NO3-N go up to 4,5 mg/L and TN increase to 9 mg/L for the first scenario. No, NO3N and TN show only increasing trend as from point loads. They don’t decrease at the
downstream because of domestic input from Develi.
Figure 7.3 No, NO3-N, NH4-N and TN variation along the stream for the first scenario
Phosphorus is formed as inorganic phosphorus (inorg P), organic phosphorus (Po)
and total phosphorus. All of them are affected by point loads along the river. They
show an increasing trend to the point loads from the headwater. Because, there are
90
agricultural inputs by assuming the model. At the point loads, their values show high
concentrations. Po increase to 0,5 mg/L, inorg P rises up to 0,75 mg/L and TP goes
up to 1,3 mg/L for the first scenario.(see Figure 7.4)
Figure 7.4 Po, inorg P and TP variation along the stream for the first scenario.
observed in Figure 7.5 along the main stream. According to the second scenario,
domestic wastewater are treated only for removing BOD 90% efficiently and
discharged into the river. When the graphics are observed, the peak point on the
91
graph corresponds to the point load contributions which increase the value of CBOD
on the main stream. The CBOD concentrations reduce at the downstream regions of
the river as the river runoff rate increases by combining of the tributaries. Ultimate
CBOD increase up to 25 mg/L for the second scenario. Its value same as first
scenario. So, it can be said that only BOD removal is not enough for water quality.
Along the main stream, DO generally fluctuate around the 8 mg/L. It falls below 8
mg/L at the intersection of the tributaries. (see Figure 7.5)
Figure 7.5 CBODu,CBODslow,CBODfast and DO variation along the stream for second scenario.
92
observed in Figure 7.6 along the main stream for the second scenario. Temperature is
around the 180C along the river. Total suspended solids (TSS) and inorganic
suspended solids (ISS) show a decrease as the river flow rates increase. At point
loads, TSS rises up to 15 mg/L and ISS goes up to 8 mg/L for the second scenario.
Figure 7.6 Temperature, ISS and TSS variation along the stream for second scenario
93
agricultural irrigation runoff. At point loads, NH4-N and No rise up to 3,5 mg/L and
NO3-N go up to 4,5 mg/L and TN increase to 9 mg/L for the second scenario. No,
NO3-N and TN show only increasing trend as from point loads. They don’t decrease
at the downstream because of domestic input from Develi. These values are same as
the first scenario like BOD.
Figure 7.7 No, NO3-N, NH4-N and TN variation along the stream for the second scenario
94
show an increasing trend to the point loads from the headwater. Because there is
up to 1,3 mg/L for the second scenario.(see Figure 7.8) These values are same as the
first scenario like BOD.
Figure 7.8 Po, inorg P and TP variation along the stream for the second scenario.
95
observed in Figure 7.9 along the main stream. According to the third scenario,
domestic wastewater are treated for removing BOD and N 90% efficiently and
the river as the river runoff rate increases by combining of the tributaries. But, in the
Figure 7.9 CBODu,CBODslow,CBODfast and DO variation along the stream for third scenario.
96
third scenario increase of CBOD is different from first and second scenario. Ultimate
CBOD increase up to 10 mg/L for the third scenario. Its value is lower than the first
and second scenario. It can be said that BOD and N removal is necessary for the
water quality of the stream.
Along the main stream, DO generally floctuates around the 8 mg/L. It falls below
8 mg/L at the intersection of the tributaries. And it increase to 9 mg/L at the
downstream.(see Figure 7.9)
Figure 7.10 Temperature, ISS and TSS variation along the stream for third scenario
97
observed in Figure 7.10 along the main stream for the third scenario. Temperature is
loads, TSS rises up to 15 mg/L and ISS goes up to 8 mg/L for the third scenario.
Figure 7.11 No, NO3-N, NH4-N and TN variation along the stream for the third scenario
98
agricultural irrigation runoff. At point loads, NH4-N rises up to 0,8 mg/L, No rises up
to 3,5 mg/L, NO3-N goes up to 4,5 mg/L and TN increase to 8,5 mg/L for the third
scenario. Their value is a bit different from other scenario. So, BOD and N removal
are not enough because of agricultural inputs. No, NO3-N and TN show only
increasing trend as from point loads. They don’t decrease at the downstream because
of domestic input from Develi.
Figure 7.12 Po, inorg P and TP variation along the stream for the third scenario.
99
up to 1,3 mg/L for the third scenario.(see Figure 12) These values are same as the
first scenario like BOD.
Figure 7.13 CBODu,CBODslow,CBODfast and DO variation along the stream for fourth scenario.
100
observed in Figure 7.13 along the main stream. According to the fourth scenario,
domestic wastewater are treated for removing BOD and P 90% efficiently and
Figure 7.14 Temperature, ISS and TSS variation along the stream for fourth scenario
101
the river as the river runoff rate increases by combining of the tributaries. In the
fourth scenario, increase of CBOD is same as third scenario. Ultimate CBOD
increase up to 10 mg/L for the fourth scenario. Its value is lower than the first and
second scenario. It can be said that BOD and P removal is useful for the water
quality of the stream.
mg/L at the intersection of the tributaries. And it increase to 9 mg/L at the
observed in Figure 7.14 along the main stream for the fourth scenario. Temperature
is around the 190C along the river. Total suspended solids (TSS) and inorganic
loads, TSS rises up to 15 mg/L and ISS goes up to 8 mg/L for the fourth scenario.
agricultural irrigation runoff. At point loads, NH4-N and No rise up to 3,5 mg/L,
NO3-N goes up to 4,5 mg/L and TN increase to 9 mg/L for the fourth scenario. Their
values are as same as first scenario. No, NO3-N and TN show only increasing trend
as from point loads. They don’t decrease at the downstream because of domestic
input from Develi.
102
Figure 7.15 No, NO3-N, NH4-N and TN variation along the stream for the fourth scenario
up to 0,35 mg/L for the third scenario.(see Figure 7.16). These values are lower than
first scenario. It can be said that BOD and P removal is very useful for the river
water quality.
103
Figure 7.16 Po, inorg P and TP variation along the stream for the fourth scenario.
observed in Figure 7.17 along the main stream. According to the fifth scenario,
domestic wastewater are treated for removing BOD, N and P 90% efficiently and
the river as the river runoff rate increases by combining of the tributaries. In the fifth
104
scenario, increase of CBOD is a bit from first scenario. Ultimate CBOD increase up
to 10 mg/L for the fifth scenario. Its value is lower than the other scenario. It can be
said that BOD, N and P removal is necessary for the water quality of the stream. It
may be one of the best scenarios.
Figure 7.17 CBODu,CBODslow,CBODfast and DO variation along the stream for fifth scenario.
105
observed in Figure 7.18 along the main stream for the fifth scenario. Temperature is
loads, TSS rises up to 15 mg/L and ISS go up to 8 mg/L for the third scenario.
Figure 7.18 Temperature, ISS and TSS variation along the stream for fifth scenario
106
to 3,5 mg/L, NO3-N goes up to 4,5 mg/L and TN increase to 8,5 mg/L for the fifth
scenario. Their value is a bit different from other scenario. BOD, N and P removal
show that water quality is not enough for N variables. No, NO3-N and TN show only
increasing trend as from point loads. They don’t decrease at the downstream because
of domestic input from Develi.
Figure 7.19 No, NO3-N, NH4-N and TN variation along the stream for the fifth scenario
107
up to 0,35 mg/L for the fifth scenario.(see Figure 20) These values show that BOD,
N and P removal is very efficient for P concentration in the river water.
Figure 7.20 Po, inorg P and TP variation along the stream for the fifth scenario.
108
observed in Figure 7.21 along the main stream. According to the sixth scenario,
domestic wastewater is not discharged into the river. So, there isn’t any point source
as domestic wastewater. This is the best scenario among the scenarios. When the
graphics are observed, the peak point on the graph corresponds to the point load
contributions which increase the value of CBOD on the main stream. This point
Figure 7.21 CBODu,CBODslow,CBODfast and DO variation along the stream for sixth scenario.
109
loads are show contribution of the tributaries. The CBOD concentrations reduce at
the downstream regions of the river as the river runoff rate increases by combining of
the tributaries. In the sixth scenario, increase of CBOD is much lower than other
scenario. Ultimate CBOD increase up to 5 mg/L for the sixth scenario. CBOD slow
and fast also have low concentrations.
Figure 7.22 Temperature, ISS and TSS variation along the stream for sixth scenario
110
observed in Figure 7.22 along the main stream for the sixth scenario. Temperature is
loads, TSS rises up to 5 mg/L and ISS go up to 3 mg/L for the sixth scenario. These
are the lowest values of the suspended solids among the other scenarios.
Figure 7.23 No, NO3-N, NH4-N and TN variation along the stream for the sixth scenario
111
to 3,5 mg/L, NO3-N goes up to 4,5 mg/L and TN increase to 8,5 mg/L for the sixth
scenario. Their constant values for all scenarios prove that there is another effect
which affects the water quality of the river.
Figure 7.24 Po, inorg P and TP variation along the stream for the sixth scenario.
112
up to 0,28 mg/L for the sixth scenario.(see Figure 24) These are the lowest values
for phosphorus.
When the scenarios are considered, it can be seen that all of them have a risk for
the water quality of the river and consequently dam water. In all graphics, there are
model chart and water quality point data obtained from IZSU sample stations. Some
of the data from IZSU show same trend with model variation. They are inorg P, NH4N, ISS, temperature, DO, CBODu, CBODslow and CBODfast. Others are not same
because of outside inputs from accepted in the model.
113
CHAPTER EIGHT
CONCLUSION
Modeling studies have been attached importance to protect the water
resources as an element of the water basin management planning. A water quality
modeling study was performed for water quality management of large river systems
where autochthonous sources (derived from with in a system, such as organic matter
in a stream resulting from photosynthesis by aquatic plants.) and denitrification play
an important role in biochemical oxygen demand (BOD) and nitrogen dynamics. In
this study, a developed model QUAL2K was used for making a good planning for
the studied basin. This model can be operated as either a steady-state or dynamic
model. In the research, the model is run under steady-state conditions and the limited
existing data are used. The water quality parameters namely DO, BOD, nitrogen, and
phosphorus are simulated and are evaluated. The investigations on the model
applications have revealed that QUAL2K is fairly flexible, comprehensive and
suitable to model of different physical conditions. QUAL2K is designed to facilitate
defensible TMDL evaluations of rivers. But QUAL2K is more complete than the
other stream programs, because it takes into account more constituents.
With regard to the model application in this sutdy, the basic problems are
related to data requirements. Existing data, which represent the physical conditions in
the basin, as in other basin in Turkey, are not adequate for obtaining realistic results.
Therefore, mostly default values are used instead of such parameters. For calibrating
the model appropriate to the studied basin, these data are required. In addition, there
is not suffecient information about domestic, industrial and agricultural wastewater
inputs. Because of these data limitations, results of the model do not realistically
represent the case of the studied basin. If appropriate and sufficient data can be
provided, an effective model simulation of water quality can be produced. In the
study, different scenarios has been developed according to different-treated domestic
wastewater discharges. Results of these scenarios are revealed the probable
conditions of the river.
113
114
REFERENCES
Chapra,S.C., & Pelletier,G.J. (2003). QUAL2K: A modeling framework for
simulating river and stream water quality: Documentation and users manual.
Civil and Environmental Engineering Dept., Tufts University, Medford, MA.,
[email protected]
Canter, L.W. (1985).River water quality monitoring. Lewis Publisher, Inc.
Dahl, M., & Wilson,D. (June 28, 2001) Modelling of water quality. Karlstad
University, Sweden.
DIE (State Statistics Institute), (1997). Population of related settlements is obtained
from personal communication.
DMI (State Meteorological Works), (2004). Meteorological data is obtained from
personal communication.
DSI (Regional Directorate of State Hydraulic Works) (1969-1986). DSI general
directorate water supply and sewage department. İzmir.
DEU(Dokuz Eylul University) & EU (Ege University ), (2000). Tahtali Basin report.
Dokuz Eylul University. Izmir.
EPA Office of Water, (1992). Total maximum daily load program.
Frey,M. & Hansen, E. (2002). Modeling and total maximum daily loads. Clean water
network. Boise.
115
Harmancıoğlu, N.B, Alpaslan, M.N., Ozkul, S.D., & Singh,V.P. (1996). Integrated
approach to environmental data management systems. Boston. Kluwer
Academic Publisher.
Harmancıoğlu,N.B., Fıstıkoğlu, O., Ozkul, S.D., Singh,V.P., & Alpaslan, M.N.
(1999). Water quality monitoring network design. Boston. Kluwer Academic
Publisher.
Hoybye,J., Iritz,L., Zheleznyak,M., Maderich,V., Demchenko,R., Dziuba,N.,
Donchitz,G., Koshebutsky,V. (2002). Water quality modelling to support the
operation of the Kakhovka Reservoir, Dnieper River, Ukraine.
Himesh,S., Rao,C., & Mahajan,A. (2000). Calibration and validation of water
quality model. India.
IZSU (Izmir Sewage and Water Authority),(1998-2000). Hydraulic and water quality
data is obtained from personal communication.
IZSU (Izmir Sewage and Water Authority),(2005). Tahtali dam and basin protection
works. Retrieved 2005, from www.izsu.gov.tr
Khandan,N.N., (2002). Modeling tools for environmental engineers and scientists.
CRC Press. London.
Kausha,S.S., Groffman,P.M., Findloy,E.G., & Fischer, D.T. Land use and changing
reactivity of organic-N and P exported from urban watersheds.
Metcalf & Eddy, Inc. (1979). Wastewater engineering:Treatment disposal reuse.
Tata McGraw-Hill publishing Company.
Orlob, Gerald T.,(1982). Mathematical modeling of water quality. International
Institute for Applied Systems Analysis.
116
Park,S.S., & Lee,Y.S., (2001). A water quality modeling study of the Nakdong River,
Korea. Elsevier Science. Korea.
Padayoa,D.O., & San Diego,M.L. (2000). Nitrogen and phosphorus in coastal
systems focus on dissolved organic N and P. Marie Science Instute. Ouezon City.
Rajar,R., Cetina,M., & Sirca,A. (1997). Hydrodynamic and water quality modeling:
case studies. University of Ljubljana, Faculty of Civil and Geodetic Engineering,
Hydraulics Department. Slovenia.
Sawyer,C.N., & McCarty,P.L. (1697). Chemistry for sanitary engineers. Boston.
International Student Edition
Stream corridor processes and characteristics. (n.d.). Retrieved 2004, from
www.nrcs.usda.gov./technical/stream-restoration
Thomann, R.V., & John, A. M. (1987). Principles of surface water quality modeling
and control. Harper and Row. New York.
Uslu, O., & Türkman, A. (1987). Water pollution and control. Prime Ministry
General Directorate of Environment Publications Educational Series I.
Van Dam,D.A., & Associates (2004). Dew point calculation chart. Retrieved 2004.
www.davandam.com
World Bank Group (WBG), (1998). Water quality models. Pollution prevention and
abatement handbook. Implementing policies of water quality management.
Water Pollution Control Regulations, (1988). Water quality Classification. General
Directorate of Environment.
117
Appendix A
Water Quality
Observations Stations
Data Sets
118
Hydraulic Characteristics Tables of Related River Tributary (IZSU,2000)
Stream
Name
13
Number
Creek
14
Number
Creek
15
Number
Creek
16
Number
Creek
17
Number
Creek
A
km2
L
(length)
m
B
(width)
m
H
(height)
m
J
(slope)
-
V
(velocity)
m/s
Q
(flow
rate)
m3/s
6,1
4800
7,0
1,0
0,01
3,0
0,052
Rectangularsection
20,4
14000
8,0
2,0
0,01
4,3
0,18
Rectangular section
39,6
14500
8,0
2,0
0,01
-
0,522
Rectangular section
17,7
11500
-
-
0,009
-
0,15
Trapezoidal
cross-section
428
-
24
2,5
0,01
4,7
3,73
Trapezoidal
cross-section
Explanation
119
Water Flow Table (DSI,1986)
Drain Area: 512 km2
Area : DSI II.Area
Station No: 6-7
Stream Name : Tahtalı Stream
Station Name : Derebogazı
Year
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
October
0,32
0,31
0,82
0,31
0,37
0,41
9,78
0,35
1,43
0,01
0,1
0,54
0,68
0,08
0,23
0,15
November
0,52
0,49
0,62
0,36
6,48
6,14
13,2
1,51
1,56
3,87
3,11
12,4
1,53
11,3
0,87
1,29
Units : 106 m3
December
61,6
7,91
0,95
3,58
44,6
17,1
40,1
10,1
2,48
20,2
21,2
14,2
11
51,7
1,48
1,27
January
28,3
15,2
6,22
1,27
56,4
6,45
38,4
77,4
53,7
55,6
12,8
26
10,1
97,6
20,3
61,5
February
60,0
59,6
21,5
21,4
40,4
13,1
20,3
84,1
26,4
15,2
34,7
22,9
33,2
53,8
6,59
52,3
March
36,0
46,3
11,9
33,0
16,7
6,14
12,3
51,5
9,43
24,6
22,4
35,4
14,2
43,6
13,7
15,4
April
11,1
16,7
4,62
5,70
7,37
16,9
5,47
38
4,29
9,11
9,04
17,3
6,5
18,5
6,91
5,15
May
3,71
4,13
2,05
1,72
4,67
4,87
2,08
8,89
2,89
6,99
6,31
10,9
2,55
6,08
3,67
3,2
June
1,49
1,94
0,88
0,69
6,78
1,73
0,63
2,06
0,96
2,29
1,84
2,92
1,5
1,03
1,4
1,92
July
0,56
0,69
0,45
0,36
0,9
0,35
0,07
0,18
0,01
0,2
0,23
0,86
0,19
0,47
0,57
0,77
August
0,33
0,45
0,127
0,34
0,26
0,38
0,30
0,01
0,02
0,00
0,02
0,03
0,19
0,23
0,17
0,15
0,28
September
0,26
0,39
0,31
0,22
0,28
0,29
0,08
0,39
0,00
0,02
0,14
0,05
0,10
0,12
0,13
0,13
Annual
-
120
Appendix B
Graphical Presentation of Water Quality
Variables in Menderes Sehitoglu
Creek
121
122
123
124
125
126
Appendix C
Water Quality Classification
for Surface Waters
127
Water Quality Classification for Surface Waters (Uslu & Turkman, 1987).
Water Quality
Water Quality Classes
Parameters
I
II
III
A. Physical and Inorganic Chemical Parameters
1. Temperature (0C)
25
25
30
2. ph
6.5-8.5
6.5-8.5
6.0-9.0
3. Dissolved oxygen(mg/l)
8
4. Dissolved oxygen
90
saturation(%)
5. Chloride (mg/l)
200
6. Sulphate (mg/l)
200
7. Ammonium nitrogen (mg/l)
0.2a
8. Nitrite nitrogen (mg/l)
0.002
9. Nitrate nitrogen (mg/l)
5
10. Total phosphorus (mg/l)
0.02
11. Total dissolved solids (mg/l)
500
12. Color (Pt-Cu unit)
5
13. Sodium (mg/l)
125
B. Organic Parameters
1. COD (mg/l)
25
2. BOD5 (mg/l)
4
3. Organic carbon (mg/l)
5
4. Total kjeldahl nitrogen (mg/l)
0.5
5. Emulsified oil and gres (mg/l)
0.02
6.Alkyl benzene sulphonate
0.05
(mg/l)
C. Inorganic Industrial Pollution Parameters
0.1
1. Hg (µg/l)
3
2. Cd (µg/l)
10
3. Pb (µg/l)
20
4. As (µg/l)
20
5. Cu (µg/l)
20
6. Cr (total) (µg/l)
*
7. Cr (+6) (µg/l)
10
8. Co (µg/l)
20
9. Ni (µg/l)
200
10. Zn (µg/l)
10
11. CN (total) (µg/l)
1000
12. F (µg/l)
10
13. Free chlorine (µg/l)
2
14. Sulfide (µg/l)
300
15. Fe (µg/l)
100
16. Mn (µg/l)
IV
>30
Out of range
6
70
3
40
6.0-9.0
<3
<40
200
200
1a
0.01
10
0.16
1500
50
125
400
400
2a
0.05
20
0.65
5000
300
250
>400
>400
>2
>0.05
>20
>0.65
>5000
>300
>250
50
8
8
1.5
0.3
0.2
70
20
12
5
0.5
1
>70
>20
>12
>5
>0.5
>1.5
0.5
5
20
50
50
50
20
20
50
500
50
1500
10
2
1000
500
2
10
50
100
200
200
50
200
200
2000
100
2000
50
10
5000
3000
>2
>10
>50
>100
>200
>200
>50
>200
>200
>2000
>100
>2000
>50
>10
>5000
>3000
128
1000
17. B (µg/l)
10
18. Se (µg/l)
1000
19. Ba (µg/l)
20. Al (mg/l)
0.3
21. Radioactivity (pCi/l)
ά – activity
1
β – activity
10
D. Organic Indusrtial Pollution Parameters
1. Phenolic matters(volatile)
0.002
(mg/l)
2. Mineral oil and
0.02
derivatives(mg/l)
3. Total pesticide (mg/l)
0.001
E. Biological Parameters
1. Fecal coliform (MS/100ml)
10
1000c
10
2000
0.3
1000
20
2000
1
>1000
>20
>2000
>1
10
100
10
100
>10
>100
0.01
0.1
>0.1
0.1
0.5
>0.5
0.01
0.1
>0.1
2000
20000
>20000
(a) The concentration of free ammonium nitrogen should not be exceeded 0.02
mg
NH3-N/l related to the value of ph.
(b) One of the value between concentration and saturation of dissolved oxygen is
enuogh to satisfy.
(c) The concentration of B should be lowered to 300 µg/l for irrigation of
conscientious plants.
(d) The concentration limits should be lowered against cloride for irrigation of
conscientious plants.
* Excessive amount
129
Appendix D
Dew Point Temperature
Calculation
130
Dew Point Temperature Calculation (D.A. Van Dam & Associates,2004)
Relative
Humidit
y
90%
18
85%
Ambient Air Temperature ( Fahrenheit)
400
500
600
700
800
900
1000
1100
1200
28
37
47
57
67
77
87
97
107
117
18
28
37
47
57
67
77
87
97
107
117
80%
16
25
34
44
54
63
73
82
93
102
110
75%
15
24
33
42
52
62
71
80
91
100
108
70%
13
22
31
40
50
60
68
78
88
96
105
65%
12
20
29
38
47
57
66
76
85
93
103
60%
11
19
27
36
45
55
64
73
83
92
101
55%
9
17
25
34
43
53
61
70
80
89
98
50%
6
15
23
31
40
50
59
67
77
86
94
45%
4
13
21
29
37
47
56
64
73
82
91
40%
1
11
18
26
35
43
52
61
69
78
87
35%
-2
8
16
23
31
40
48
57
65
74
83
30%
-6
4
13
20
28
36
44
52
61
69
77
20
0
30
0
Surface Temperature at which Condensation Occurs
Dew Point,
Dew point temperature means amount of moisture in the air. It is the temperature
to which the air would have to cool at constant pressure and constant water vapor
content in order to reach saturation. Saturation means that air is holding max amount
of water vapor possible at the existing temperature and pressure. Temperature at
which moisture will condense on surface. No coatings should be applied unless
surface temperature is a minimum of 5° above this point. Temperature must be
maintained during curing.
Example,
If air temperature is 70° F and relative humidity is 65% the dew point is 57°F. No
coating should be applied unless surface temperature is 62°F minimum.
131
For Studied Model,
Air temperature is 20 0C around the basin in Spring months. Relative
humidity is 66% in Menderes. These data were obtained from meteorology
stations(DMI,1998).
If air temperature is changed into Fahrenheit from Celsius, the temperature is
68 0F from the equation. Equation is,
1.8 x (0C) + 32 = 0F
When relative humidity (%66) and the temperature (68 0F) are examined in
the dew point calculation chart, dew point temperature is 58 0F. If it is changed into
Celsius, it is 14.4 0C.
As a conclusion, dew point temperature is 14.4 0C.
132
Appendix E
Domestic Waste Water
Characteristics
133
Typical Composition of Untreated Domestic Wastewater (Metcalf & Eddy,1979)
Constituent
Total Solids
Dissolved, total,
Fixed
Volatile
Suspended, total
Fixed
Volatile
Settleable solids,mL/L
BOD5, (5-day,200C)
Total organic carbon (TOC)
COD
Nitrogen (total-N)
Organic
Free ammonia
Nitrites
Nitrates
Phosphorous (total-P)
Organic
Inorganic
Chloridesb
Alkalinity (as CaCO3)b
Grease
Strong
1200
850
525
325
350
75
275
20
400
290
1000
85
35
50
0
0
15
5
10
100
200
150
Concentration
Medium
720
500
300
200
220
55
165
10
220
160
500
40
15
25
0
0
8
3
5
50
100
100
(All values wxcept settleable solids are expressed in mg/L)a
a
mg/L = g/m3
b
Values should be increased by amount in domestic water supply.
Note : 1.8 (0C) + 32 = 0F
Weak
350
250
145
105
100
20
80
5
110
80
250
20
8
12
0
0
4
1
3
30
50
50
134
Appendix F
Land Use Distribution of
Tahtali Dam Basin
135
Tahtali Dam Basin Land Use Distribution According to Protection Area in 2000
(Agricultural Ministry State Directorate, 2000)
895
110
-
-
2890
200
5150
4600
2915
7515
470
117
250
237
608
2300
3083
100
150
2143
200
6706
1162
16
35707
151923
21171
7540
1629
4
3620
2526
19315
52173
5904
736
15470
3450
201808
Cotton
Others
-
Cereals
20
Tobacco
20
Greenha
ouse
10
Others
(include
d citrus)
190
Olive
1236
Tot.
200
Wet
Open
Vegetable
1036
Dry
Short
Distance
Medium
Distance
Long
Distance
Fruit
Vineyards
Agricultural Area
Unused
Agricul.
Area
Pastu
-re
Forest
136
Tahtali Dam Basin Land Use Distribution (da) (Agricultural Ministry State Directorate, 2000)
Agriculture Area
Fruit
Cereals
Cotton
Others
12.500
11.500
24.000
160
-
30
125
191
24
5700
12456
4200
214
3230
-
200
50530
Gorece
8.250
50
8.300
1.234
-
23
2300
-
-
100
4820
-
23
841
10396
200
28237
Greenhouse
Oglananasi
yards
Open
Area
Name
Olive
Tobacco
Total
Others
Land
Citrus Fruits
Forest
Agr.
Total
Unused
Wet
Vine
Dry
Village
Vegetable
Pasture
Area
Kısık
2.450
930
3.350
200
-
-
-
-
6
-
3099
-
55
795
2717
120
10342
Catalca
7.600
1.500
9.100
2520
150
373
1000
787
23
400
3800
-
47
948
31545
100
50793
Akcakoy
3.150
350
3.500
2040
50
148
100
130
8
100
710
3
9
920
2173
100
9991
Yenikoy
8.185
2.315
10.500
1190
-
65
3965
397
68
500
4237
-
100
291
32152
180
53645
Develi
4.600
2.915
7.515
470
-
117
250
237
608
2300
3083
100
150
2143
6706
200
23879
Degirmendere
4.740
3.500
8.240
2620
100
145
500
196
448
1280
2612
200
110
1413
11068
270
29202
Kuner
6.000
1.300
7.300
400
-
28
100
211
126
3170
5400
-
75
1550
14044
80
32484
Sasal
1.036
200
1.236
190
-
10
20
20
-
895
110
-
-
2890
5150
200
10721
Mend. merk.
13.270
9.840
23.110
2410
-
291
7335
74
1811
1725
6510
211
78
2081
6173
400
52209
Kaynaklar
42.477
146
42.623
1157
-
1806
327
290
1
200
4228
-
10
920
35040
-
86602
Karaağaç
2.150
1.734
3.884
1120
-
1232
-
470
8
1000
2071
1010
-
1152
6820
-
18767
Kırıklar
1.953
-
1.953
1279
-
1385
62
344
-
700
230
-
-
576
130
-
6659
Belenbasi
1.632
42
1.674
740
-
1222
30
100
1
1000
500
50
-
200
13050
-
18567
Demirci
1.889
2.500
4.389
1027
-
92
-
50
-
2540
500
30
-
150
8000
800
17578
14548
Yogurtcular
1.824
674
-
300
250
-
-
400
300
-
-
200
10000
600
Yesilkoy
1.500
100
-
40
-
380
2
200
500
200
15
63
1500
400
4900
Dogancilar
3.200
2300
-
60
200
-
-
300
200
-
-
140
17000
-
23400
167.198
21831
300
7367
16564
3877
3134
22510
55366
6004
886
20503
213664
3850
543054
TOPLAM
121.852
38.822

MODELING OF WATER QUALITY IN A DRINKING WATER BASIN

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