modelling ngong river final project 2010

JOMO KENYATTA UNIVERSITY OF

AGRICULTURE AND TECHNOLOGY.

DEPARTMENT OF CIVIL, CONSTRUCTION AND

ENVIRONMENTAL ENGINEERING.

MODELLING WATER QUALITY OF

THE NGONG’ RIVER

PREPARED BY:

KRHODA MICHAEL OKOYE

(E25-0177/05)

FINAL YR PROJECT FOR BSC. CIVIL, CONSTRUCTION

AND ENVIRONMENTAL ENGINEERING.

23RD November 2010

MODELLING WATER QUALITY OF THE NGONG’ RIVER

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ACKNOWLEDGMENTS

I take this opportunity to thank the Almighty God for health and provision throughout this

period as I worked on this project. Let this be testament of His goodness for His glory both in

this life and in the life eternal.

I thank Marian Kioko at NEMA for the information and assistance that she afforded me during

the initial stages of the project. I thank Isaac Muraya at City Hall for the background

information about the Nairobi River Basin Programme. I thank Mrs Kibetu from Jomo

Kenyatta University of Agriculture and Technology (JKUAT) Dept. of Construction, Civil and

Environmental Engineering, for her wise council throughout the period that I consulted with

her about the project work. I thank Mr Kibe from the Civil Environmental Lab for the

assistance in carrying out the testing of the samples. I thank Wambia Waigwa and Paddy

Mulweye for their selfless help during sample collection.

Thank you Prof. Krhoda and Mrs Krhoda for everything you have done for me to make this

possible.


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DECLARATION

“I KRHODA MICHAEL OKOYE do solemnly declare that this report is my original work and to the best of my knowledge, it has not been submitted for any degree award in any University or Institution.”

Signed……………………………… (Author)

Date……….………………………..

E25-0177/05

CERTIFICATION

“I have read this report and approve it for examination.”

Signed……………………………………… (Supervisor) Date………….……………………………..

MRS. KIBETU.


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Table of Contents List of abbreviations: ................................................................................................................... 7

MODELLING THE WATER QUALITY OF THE NGONG' RIVER. ....................................... 8

1. INTRODUCTION ............................................................................................................... 8

1.1 Background: ............................................................................................................... 8

1.2 Study Justification: ..................................................................................................... 9

1.3 Problem Statement: .................................................................................................. 10

1.4 Objectives: ................................................................................................................ 10

1.4.1 Overall objectives: ........................................................................................... 10

1.4.2 Specific objectives: .......................................................................................... 10

1.5 Research Hypothesis: ............................................................................................... 10

1.6 Limitations of the research: ...................................................................................... 11

2. LITERATURE REVIEW .................................................................................................. 13

2.1 Context of modelling ................................................................................................ 13

2.2 General geographic information ............................................................................... 15

2.3 General pollution information .................................................................................. 17

2.4 Description of the river basin ................................................................................... 20

2.4.1 The IPU section ............................................................................................... 20

2.4.2 The CPU section .............................................................................................. 21

2.4.3 The MPU section ............................................................................................. 23

2.5 Hydrological measurements ..................................................................................... 24

2.6 Nutrients in the Ngong River.................................................................................... 25

2.6.1 Nitrogen compounds ........................................................................................ 25

2.6.2 Phosphorus compounds ................................................................................... 30

2.7 Results from previous studies and the gap that exists .............................................. 31

3. RESEARCH METHODOLOGY ...................................................................................... 36

3.1 Orientation ................................................................................................................ 37

3.1.1 Data collection methods................................................................................... 37

3.1.2 Measuring stream flow: ................................................................................... 37

3.1.3 Sampling: ......................................................................................................... 39

3.1.4 Materials and methods of water quality analysis ............................................. 39

3.2 Formulation of relations between variables and parameters ..................................... 50

3.3 Non-dimensionalization ........................................................................................... 50


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3.4 Solution of model equations ..................................................................................... 51

3.5 Preliminary test application ...................................................................................... 51

3.6 Model Verification ................................................................................................... 51

3.7 Reiteration of steps 2-6 ............................................................................................. 51

3.8 Implementation ......................................................................................................... 52

4. DATA COLLECTION/ SAMPLING ............................................................................... 53

4.1 Catchment characteristics ......................................................................................... 53

5. SAMPLE RESULTS AND DATA ANALYSIS .............................................................. 59

5.1 Longitudinal Profile.................................................................................................. 59

5.2 Cross-sectional Profile .............................................................................................. 60

5.3 Biochemical Oxygen Demand .................................................................................. 60

5.4 Ammonia .................................................................................................................. 62

5.5 Nitrite ....................................................................................................................... 64

5.6 Nitrates ..................................................................................................................... 66

5.7 Phosphates ................................................................................................................ 69

5.8 Comparison of results from 2003 with 2010 ............................................................ 71

6. MODELLING ................................................................................................................... 75

6.1 THE WATER QUALITY MODEL ......................................................................... 75

6.1.1 Model Network ................................................................................................ 75

6.1.2 Model Inputs: ................................................................................................... 75

6.1.3 Hydraulic Calculations: ................................................................................... 78

6.1.4 Water quality calculations ............................................................................... 79

6.2 Solution to model equations and Preliminary test application: ................................ 83

6.2.1 BOD ................................................................................................................. 83

6.2.2 Ammonia ......................................................................................................... 83

6.2.3 Nitrate .............................................................................................................. 83

6.2.4 Nitrite ............................................................................................................... 84

6.2.5 Phosphates ....................................................................................................... 84

6.3 Model Verification and implementation: .................................................................. 84

6.4 Accounting for pollution input into the river: ........................................................... 89

6.5 Capabilities and Limitations of the Water Quality Model ........................................ 89

6.6 Discussion: ............................................................................................................... 90

6.7 Conclusions: ............................................................................................................. 90

7. RECOMMENDATIONS AND WAY FORWARD ......................................................... 91

8. REFERENCES: ................................................................................................................ 92


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9. APPENDICES: ................................................................................................................. 95

9.1 Budget: ..................................................................................................................... 95

9.2 Working schedule: .................................................................................................... 96

9.3 The tabulated results from NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report: ........................................................................................................................... 97

9.4 The plotted results from NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report: ........................................................................................................................... 98

9.5 Manning’s co-efficient (Chow, 1959): ..................................................................... 99

9.6 Summary output from regression analysis for modelling: ...................................... 100

9.6.1 BOD ............................................................................................................... 100

9.6.2 Ammonia ....................................................................................................... 101

9.6.3 Nitrates .......................................................................................................... 102

9.6.4 Nitrites ........................................................................................................... 103

9.6.5 Phosphates: .................................................................................................... 105

9.7 SISMOD OPERATION ......................................................................................... 106


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List of abbreviations: NRBP: Nairobi River Basin Programme

UNEP: United Nations Environmental Programme

UN- HABITAT: United Nations Habitat

UoN: University of Nairobi

AWN: Africa Water Network

TN: Total Nitrogen

TP: Total Phosphates

CBD: Central Business District

IPU: Individual Polluting Unit

CPU: Collective Polluting Unit

MPU: Mega Polluting Unit

NO3-: Nitrate ion

NO2-: Nitrite ion

NH4-: Ammonium ion

N2: Molecular nitrogen

N2O: Nitrous oxide

PO43-: Phosphate ion

HCl: Hydrochloric acid

NaOH: Sodium hydroxide

H3BO3: Boric acid

SISMOD: Simple Stream Model


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MODELLING THE WATER QUALITY OF THE NGONG' RIVER.

1. INTRODUCTION

1.1 Background: If real life problems are attacked using mathematics, a ‘translation’ is needed to put the subject

into mathematically tractable form (Mooney, 1999). Modelling is the description of an

experimentally verifiable phenomenon by means of the mathematical language where we have

2 classes of quantities:

• Variables: we distinguish between dependent and independent variables.

• Parameters: these are used to link variables to each other. Are either constant or

adjusted by the experimenter.

Water quality models are important decision support system tools for water pollution control,

study of the health of aquatic ecosystems and assessment of the effects of point and diffuse

pollution (Bende-Michl et al, 2009). A mathematical stream water quality model with the

following specifications is used:

The model is mechanistic. This means that it is derived from the mathematical abstraction of

physical phenomena such as mass balance, transport and reaction kinetics, to allow the users to

construct mass balances on stream locations with discharges, diffuse loads and stream

junctions. A mechanistic model also provides the opportunity to give the users an introduction

to the essential processes in stream pollution and purification (Erturk et al., 2006).

The model is steady state. This means that can only characterize a system after it has reached

the steady state, and is therefore relatively easy to run (Erturk et al., 2006).

The model is a spatial model. It considers the spatial heterogeneity of the system. It solves the

water-quality related equations in one dimension that is defined along the stream in flow

direction (Erturk et al., 2006).

The model solves the water quality equations analytically. Analytical models use the exact

solution of systems equations and are therefore applicable to special simple cases, where an

analytical solution exists for the model equations.

According to the United Nations Environmental Programme, Nairobi Rivers are increasingly

chocking with uncollected garbage, human waste from informal settlements; industrial waste in

the form of liquid effluence and solid waste; agrochemicals, and other waste, especially petro-


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chemicals and metals from micro-enterprises – the “Jua-kali”; and overflowing sewers. These

pollutants change in position and momentum as they flow within the water body. Of particular

concern is the Ngong’ River, a tributary of the Nairobi River.

This pollution situation has occasioned spread of water-borne diseases, loss of sustainable

livelihoods, loss of biodiversity, reduced availability and access to safe potable water, and the

insidious effects of toxic substances and heavy metal poisoning which affects human

productivity.

The Nairobi River Basin programme (NRBP) was established as a multi-stakeholder initiative

to bring together the Government of Kenya, UNEP, UN-Habitat, UNDP, the private sector and

civil society with a vision to restore the riverine ecosystem with clean water for the capital city

and a healthier environment for the people of Nairobi. One of the objectives of NRBP was to

rehabilitate, restore and manage the Ngong’ River ecosystem. Phase I of the programme

(October 1999 to March 2000) constituted a situation assessment of water quality, status and

impact of pollution, a project that was implemented by the Africa Water Network (AWN).

Phase II of the NRBP was conducted to the Ngong/Motoine river to provide information for

the pilot project to identify major point sources of pollution.

1.2 Study Justification: The purpose of this study is the development of a water quality model and to determine the

biological and chemical characteristics and the concentration of constituents in the river water

at different locations along the course of the river and provide data for an understanding of the

nature of the river water is essential in the management of environmental quality.

Freshwater management challenges are increasingly common. Limited resources of the Ngong’

River are allocated between agricultural, municipal and industrial use. It is thus necessary to

determine pollution generation, discharge, flows and in-stream water quality under the current

uses of the water.

An analysis of the loading data of the Ngong’ River will allow the researcher to suggest any

pollution reduction measures that can be undertaken to restore the quality of the water of the

Ngong’ River.

The section of the Ngong’ River to be considered is the profile from Karaini Dam to a location

where it drains out to the Industrial Area at Outer Ring road bridge. This area is of particular

interest in order to determine the effect of the presence of informal human settlement and

industrial processes on the quality of the water of the Ngong River flowing through the area.


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Increasing industrialisation and the growth of large urban centres have been accompanied by

increases in the pollution stress on the aquatic environment.

1.3 Problem Statement: Models are necessary to monitor and analyse the current pollution levels of the Ngong’ River

and to determine the ability of the river to naturally dilute the pollutants because it directly

affects the livelihoods, biodiversity and availability of potable safe water for its environs.

1.4 Objectives: Objectives are broken down into overall and specific objectives.

1.4.1 Overall objectives: a. To investigate the current quality of the water in the Ngong’ River.

b. To carry out water quality modelling.

1.4.2 Specific objectives: a. To obtain samples of water from points along the longitudinal profile of the

Ngong’ River and carry out an analysis of the quality of the water.

b. To identify the level of pollutants in the Ngong’ River.

c. To determine the change in water quality by comparison of concentration of

BOD and nutrient constituents along the Ngong' River profile.

d. To determine the sources of these pollutants in terms of activities or agents

such as industries, informal settlements.

e. To undertake water quality modelling for the Ngong’ River.

f. To determine the monitoring needs and suggest possible pollution reduction

measures that can be taken to restore the quality of the water in the Ngong’

River.

1.5 Research Hypothesis: The concentrations of pollutants in the Ngong’ River has risen since AWN did their assessment

for NRBP in February- November 2003.


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1.6 Limitations of the research: a) Scope of research: the scope of this research project is limited to

a. Biological Oxygen Demand (BOD)

b. Total Nitrogen (TN) - nitrates, nitrites and ammonia.

c. Total phosphates (TP)

b) The water quality modelling will be done through the use of the diffusion equations or

finite difference methods. The prototype is a continuum of constituents and processes.

Simulation of such a system on a computer requires representation in a discrete

fashion.

c) Sampling points shall be limited to positions at the following locations:

1. Sample site 1: Kariani Dam

Objective: identification of baseline conditions in the water course system

2. Sample site 2: Jamhuri Park dam outlet

Objective: selection of a point to determine the change in baseline conditions

before entry into Kibera Slum.

3. Sample site 3: Kibera bridge

Objective: selection of a point to evaluate the effect of informal human

settlement on the quality of the river water.

4. Sample site 4: weir at the outlet to Nairobi Dam

Objective: to assess and determine the difference in the water quality after

stabilisation in Nairobi Dam.

5. Sample site 5: Dunga Road Bridge.

Objective: selection of a point to determine the extent and effect of waste

discharges from industrial establishments

6. Sample site 6: Outer-ring Road Bridge


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Objective: selection of a point to determine the extent and effect of waste

discharges from industries.

d) Model verification will be done with only 1 test data due to temporal constraints.


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2. LITERATURE REVIEW

2.1 Context of modelling Water quality changes in rivers are due to physical transport processes and biological,

chemical, biochemical, and physical conversion processes.

The above processes in the water phase are governed by a set of extended transport equations

that can be represented conceptually in the diagram below (Reichert et al., 2001):

=+ + +

Diagram 2.1: shows the transportation process of pollutants in a natural water system.

Advection is the transport mechanism of a substance or the conserved property, by a fluid, due

to the fluid’s bulk motion in a particular direction e.g. the transport of pollutants in a river. The

motion of the water carries these impurities downstream. The fluid motion in advection is

described mathematically as a vector field and the material transported is typically described as

a scalar concentration of substance. Advection requires currents and thus can only take place in

fluids. The advection equation is the partial differential equation that governs the motion of the

conserved scalar as it is advected by a known velocity field.

Diffusion is the spread of particles through random motion from the regions of higher

concentration to regions of lower concentrations. The time dependence of the statistical

distribution in space is given by the diffusion equation which is a partial differential equation

which describes density fluctuations in a pollutant undergoing dispersion.

Conversion is the process by which the pollutant under investigation is broken down by

biological, chemical, biochemical, and physical conversion processes.

A modification of a mathematical stream water quality model called SISMOD (Simple

Stream Model) with the following specifications is used (Erturk et al., 2010):

The model is mechanistic. This means that it is derived from the mathematical abstraction of

physical phenomena such as mass balance, transport and reaction kinetics, to allow the users to

construct mass balances on stream locations with discharges, diffuse loads and stream

junctions. A mechanistic model also provides the opportunity to give the users an introduction

to the essential processes in stream pollution and purification.

Change in concentration

Change due to advection

Change due to diffusion or dispersion

Change due to conversion processes


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The model is steady state. This means that can only characterize a system after it has reached

the steady state, and is therefore relatively easy to run.

The model is a spatial model. It considers the spatial heterogeneity of the system. It solves the

water-quality related equations in one dimension that is defined along the stream in flow

direction.

SImple Stream MODel (SISMOD) is modelling software that can conduct simple hydraulic

and water quality calculations along a stream in flow direction. It is easy to use and is designed

such a way that it can be integrated with other software (Erturk, 2009).

The water quality model developed in this study is a preliminary model adapted from SISMOD

that mainly aims at supporting the water quality assessment. For the purposes of this

experiment the modelling process will be applied.

SISMOD solves the relevant equations analytically- Analytical models use the exact solution

of systems equations and are therefore applicable to special simple cases, where an analytical

solution exists for the model equations; however some intermediate calculations are conducted

using numerical algorithms. Water quality calculations are conducted step by step and serially

with hydraulic calculations. The model will simulate five water quality variables including

biochemical oxygen demand, ammonium nitrogen, nitrate nitrogen and phosphate phosphorus

for primarily aerobic and conditions.

There are three types of reaches in SISMOD model network. These are defined as;

a) Headwater reach: the beginning of the streams or in model network they constitute the

beginning of the model network. In a model network several headwater reaches can be

defined.

b) Standard reach: a regular reach with no specific characteristic

c) End reach: the reach where the model network ends and all the flow goes out of the

systems. In the model network, there can only be one end reach.

Other definitions that are important in the operation of SISMOD are:

a) Diffuse Load without Flow: These are the diffuse source loads without flow that are

entering the stream reach and are in unit of kg.km-1.day-1. Diffuse source loads

without flow should be defined for each stream reach. If there are no diffuse sources

without flow for a water quality parameter in a stream reach, than it should be defined

as zero kg.km-1.day-1.


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b) Diffuse Load with Flow: These are the diffuse source loads with flow that are entering

the stream reach. For diffuse source loads with flow, flows and the concentrations of

each simulated water quality parameter should be defined. Diffuse source loads with

flow should be provided to the model for each stream reach. If there are no diffuse

sources with flow for a water quality parameter in a stream reach, than it should be

defined as zero.

2.2 General geographic information There are three main tributaries that flow through Nairobi City’s Central Business District

(CBD), namely River Nairobi, River Mathare and River Ngong, all of which are subjected to

extreme levels of pollution ranging from agricultural fertilizers and raw domestic sewage, to

industrial waste. A general map of Ngong' River showing the relative positions of the sampling

stations along the profile is shown on the page that follows.

Diagram 2.2: Map of Ngong' River and the sampling stations along the profile of the river.


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In most cases, solid and liquid waste from these sources are discharged directly into the river

system having undergone no treatment whatsoever, thereby severely damaging the river

ecology as well as posing severe risks to human health. The rivers themselves are now

considered an environmental health hazard due to the high concentrations of chemical and

bacteriological toxic waste. Despite this, nearly half of the urban population are at one time or

other, dependent on them as a source of water for domestic use and in the worst cases, for

drinking (Kahara, 2002).

Most heavily affected are the urban poor, who are also reliant on the sewage lines for irrigation

of vegetables and other crops that they grow within the city as a source of income.

Unfortunately, many of the city's sewage lines are deliberately damaged or blocked in order to

obtain the nutrient rich water for agriculture. Untreated industrial effluents, raw sewage and

waste (liquid and solid) from human settlements situated along the rivers have severely

impacted the rivers quality and quantity, resulting in eutrophication, proliferation of hazardous

microbes and acute chemical stress on the aquatic ecosystem (Kahara, 2002).

Increased discharges of mostly untreated or poorly treated municipal waste water from sewage

systems in the city have plainly turned these rivers into open sewers. Industries within Nairobi

that have very poor waste treatment, if any, are discharging their waste waters into the existing

municipal sewerage system and/or directly into the rivers. Non-biodegradable waste

accumulates, thus overloading the system effectively reducing its self-purification capacity

(NRBP-UNEP, 2000). The water supply of Nairobi was initially designed to serve a population

of a few thousands; however, it has become increasingly clear that the system is inadequate to

serve the current population of over two million.

Considering the number of projects involved, it is a national dilemma as to if there has been

any improvement at all.

A picture of the polluted Ngong' River is in the page that follows:


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Diag 2.3: a heavily polluted Ngong' River coursing through Nairobi’s Eastlands area after

Industrial area (October 2008).

2.3 General pollution information Nairobi River has several tributaries, namely Motoine/ Ngong River, Nairobi River and the

Mathare River. In a study conducted for the Nairobi River Basin Project in the year 2000, the

sources (namely, Ngong' and Dagoretti forest, Ondiri/Kikuyu wetlands and Mathare catchment

area respectively) were observed as being generally clean and free of pollution. Farmers around

the Ondiri Swamp at the source of Nairobi River, use the water to irrigate land and plant

vegetables as well as other crops. They also use the water for drinking and watering their

animals. Pollution of the rivers becomes most apparent as it flows through the slum areas and

finally reaches alarmingly high levels in the industrial areas.

It is important to note that almost half of the urban population live in unplanned settlements

(slums), which for the most part lack basic water and sewerage facilities. It is not surprising

that these communities are established next to the rivers (Ndede, 2002).

Numerous studies have already been conducted over the past two decades (see Ohayo et al.

1996, Wandiga 1996, Olago et al. 2000, Issaias 2000, Kithaka 2001), to assess the rivers’ water

quality and results indicate that the levels of pollution are rising progressively between the

source and the industrial area. In each case study the main reasons for pollution in the rivers

were identified, and all agree on the following;


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1. Non-implementation of legislation to protect urban water resources.

2. Intentional or accidental blockage of sewer lines and manholes for various reasons.

3. Absence or poor planning of settlements along rivers and water bodies.

4. Acute shortage of funds in Local Government Authorities to sort out the problems.

The most important consideration of the problem lies in its diversity, and the fact that the range

of river pollution is very broad in terms of pollutants and the area affected (NRBP- Phase II).

Various studies have been conducted on the Ngong' River and its environs, and from them,

attempts have been made to develop a strategy through which the problem can be classified

and adequately tackled.

Although much of the data that has been collected thus far has tended to be both spatially and

temporally disjointed (due to lack of a basic monitoring criterion), it has provided a enough

base to assume a general pollution trend. The data collected presents the possibility that there

may be three or more basic categories of anthropogenic pollution sources or groups of

polluters, from the time the rivers begin to be of financial or social use, till they depart from the

CBD. Each category or group of polluter presents a set of problems, which are by comparison,

very diverse from the other. No attempts have been taken before to detail different

methodologies that can capture the diverse pollution categories adequately. It is appreciated

here that for sufficient statistical analysis, the sampling methodology and overall long term

monitoring design should be adjusted to provide an objective and realistic basis for assessment

(Kahara, 2002).

It becomes clear that no matter how much effort is placed on rectifying the pollution at one

section or category of pollutants; this strategy will ultimately be cancelled by the other groups

of polluters .Each case is different and therefore requires a slightly modified approach. While it

would be an immense and economically unworkable task to deal with individual problems, the

categories of pollutants would each contain those polluters displaying similar traits in terms of

demography, types of pollution released, socio-economic structure and overall effect on the

ecology (Kahara, 2002).

The basic groups herein referred to as Polluting Units, can be divided theoretically into the

following (Kahara, 2002):

1. Individual Polluting Units (IPUs),

which are found in the upper reaches of the river basin, where the population densities

and population growth rates are relatively low. Their main activities include crop


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farming and animal husbandry which usually results in low to medium pollution due

to limited agricultural chemical and fertilizer usage. Other problems such as high

turbidity may occur due to soil erosion arising from deforestation and poor land

management practices. Removal of riparian vegetation could also increase risks of

flash floods downstream. Many of the problems in such areas can be solved through

education and awareness programmes to suit the needs of the area and to improve land

management.

2. Collective Polluting Units (CPUs),

which are usually found within the city limits, and are characterized by high

population density and high growth rates, mainly resulting in unplanned settlements,

which encroach on the river-banks. The level of pollution produced in such areas can

be equated to several IPUs both in quantitative and qualitative effect on the river

ecology. Here the main pollutants are domestic organic waste (equivalent in

composition to fertilizer/manure), and large amounts of non-biodegradable solid waste

with high plastic content. The population in such areas is highly dynamic and

therefore it may be difficult for awareness/ education campaigns to effect sustainable

changes in residents’ beharviour. CPUs may require a more technical approach such

as planning connections to main sewerage lines and the establishment of definite

guidelines that can be enforced by the Community Based Organisations (CBOs) in the

area. These areas also require a greater support and cooperation from the Local

Authorities to be able to plan effectively.

3. Mega-Polluting Units (MPUs),

Include large-scale manufacturers and industries, which discharge pollutants in vast

quantities and high concentrations directly into the river water. This may be done

intentionally or as a result of faulty sewer lines, which require unblocking or

upgrading. The pollutants produced here are usually rich in toxic chemicals and heavy

metals, as well as high concentration of organic waste. It is expected that production

rate may increase to cater for an overall growing population (market), thus resulting in

more pollution being released. These are cases of non-implementation of legislation

governing waste treatment before being discharged into water sources. In some areas

the main problem occurs due to blocked, overflowing sewers, a problem which simply

requires rectification by the Local Authorities. The process of unblocking the sewers

is also hampered by the lack of facilities, therefore it could be suggested that the

industries take it upon themselves to contribute to the correction of sewer breakages

and take responsibility.


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2.4 Description of the river basin The study river subject to this project is the NGONG RIVER with a total catchment area from

the source to the confluence with Nairobi River of about 127 km2. The source of Ngong' River

is Motoine swamp and Dagoretti forest, possibly from springs issuing between lava flows with

differing porosities and permeability.

The basin comprises of various land use types, namely forest, grasslands, farmlands, limited to

flood plains and around the dam; and built area, including buildings and roads. The following

are descriptions of the river basin specifically as it relates to the proposed Polluting Units. The

entire river basin is about 42.3 km long and narrow.

2.4.1 The IPU section The IPU section stretches on the upper sections of Ngong/Motoine River. From the source, the

river flows through a series of four man-made dams before River Motoine crosses Ngong Road

Bridge, and two larger dams at the Race Course. A small tributary from Ngong forest joins the

Motoine River near the Race Course, before it flows into Nairobi Dam marking the southern

boundary of the Kibera informal settlement (CPU section).

Up to the dam where the CPU section begins, the river basin is about 26.7 km long and not

more than 5 km at its widest breadth. The river channels are steep, V-shaped cross-sections as

a result of the continued uplift and deposition of lava and tuff and deeply incised and or re-

excavated within their own valleys.

Erosion and sediment deposition in the upper reaches

Sediment is produced wherever soil is exposed to rainfall energy and flowing water. Erosion

from farms, gardens, roads as well as footpaths are common. Other sediment sources include

construction sites, earth-lined channels and mass wasting processes including avalanche,

landslides and mudflows. Most of these sediment drains into the dam, reducing the storage

capacity of the dam. The problem of erosion needs to be addressed throughout the river basin,

but most urgently, in the upper reaches where deforestation and encroachment onto the riparian

way leave has led to heavy soil erosion.

Activities along the river

The Motoine River rises from Riu Swamp and is heavily used in the settled Dagoretti area. As

the river flows eastwards (mainly underground) for most of its course, farmers in the valley


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impound its water for irrigation agriculture and several other domestic uses. The water colour

in this section of the river is mainly red due to the soil characteristics of the area. Other

polluting activities include dairy farming and abattoirs. Therefore, the Motoine River starts

receiving agrochemical pollution right from its head water in the Dagoretti area, and picks

other forms of pollution as it flows through the Ngong' Forest and the Kibera area. A second

IPU section may exist after the Industrial area, approximately 20 km up to the confluence with

the Athi River.

2.4.2 The CPU section The stretch from the Nairobi dam outlet to the confluence with Nairobi River is about 21.0 km

long. From the Nairobi Dam spillway right through the Langata Road Bridge flows through

concrete and lined channels. River-bank erosion was noted only at the Langata Road Bridge

where there was no concrete lining. The impact of concrete lining on groundwater recharge to

the adjacent floodplain and discharge to the stream during the dry season are not known,

however the lack of lateral connectivity is very likely to have an effect on the water quality.

Further downstream of the confluence down cutting has incised the river valley to about 15 to

30m deep.

Hydrological regime of Nairobi dam

The Nairobi dam was constructed in the late 1940's as a source of fresh drinking water for the

city of Nairobi. The Nairobi Dam is shallow; at the time constructed it had a surface area of

about 356,179 m2 and a volume of 98,422m3. The average depth of the dam was 2.76m. The

dam inlet is about 1700m while the dam crest is about 1680m above mean sea level. The dam

is currently heavily silted by sediments from erosion and solid waste dumped at various places

to reclaim land for agriculture. Water hyacinth (Eicchornia crassipes) as well as various other

aquatic macrophytes, such as common reeds and bulrushes, have infested the water body

disrupting fisheries and recreation (Issaias 2000).

Over the past decade the dam has reached hyper-eutrophic levels and is generally of little

socio-economic use to the city, despite its unique position in the CBD (Kahara, 2002). It is

nevertheless an essential part of the river course as a number of biochemical reaction take place

within the anaerobic water column.

Over dam precipitation is about 875mm per annum on a surface area of 356,179 m2. The

evaporation rate is about 1750mm per annum as temperature and wind velocity increase while


22 E25-0177/05

relative humidity decreases towards the lower part of Nairobi (Ohayo et al. 1996).

Evapotranspiration from the water hyacinth may be higher than potential evaporation. An

estimate of 1.13 times the rate of potential evaporation has been adopted for the water balance

calculation.

The densely populated settlements beside the dam are the most important source of both solid

and liquid pollution, but it is possible that the river can purify most of the domestic pollutants

naturally given adequate amounts of time. Solid waste is much more difficult to deal with and

this has led to a dramatic decrease in the residence time within the reservoir. Change on water

storage in the dam may be measured by continuous recording of water levels over the years.

Currently, there is no record.

However for Nairobi Dam rearranging and solving for outflow through Ngong River, we

obtain:

Qm = P + R – dS – E Equation 2.1

Where Qm = discharge

P = precipitation

dS = change in storage

E = evapotranspiration

Rearranging and simplifying, the change in storage, dS, is negligible. It is important to note

that none of the studies have as yet properly addressed the problem of water and mass balances

for the rivers reservoirs, and most are simply estimates from old data. For the purpose of

management it is suggested that a clear record of the reservoir mass/water balance be kept in

order to monitor the input of pollutants and the changes undergone during their residence in the

dams.

Activities along the Nairobi dam to the confluence with Nairobi River

The Motoine River is the main inlet into the Nairobi Dam, but other streams and springs

discharge into the Dam as well (Ndede 2002). Runoffs from the impervious surfaces, such as

iron sheet roofs of the Kibera settlement also contribute significant amounts of flow into the

Dam, especially during rainstorms. The amount of discharge from within the catchment into

the Dam at base flows is about 0.5 cumecs, including underground seepage. The amount

leaving the Dam through the spillway as the Ngong River is variable. During dry years, it


23 E25-0177/05

becomes a mere trickle, but when there are heavy rains, it floods. In November 2001, the flow

from spillway was measured at 0.2 cumecs (Ndede 2002).

Various activities such as car washing, small-scale industry and urban farming have been

observed in this area, all of which are dependent on the river water both as a source as well as a

drainage system.

Due to lack of a waste management mechanism for Kibera slum, the Motoine River system has

become a natural receptacle for all the uncollected waste emanating from the area. Dumping of

solid waste is serious at bridges and crossing points. Drainage systems within the slums have

also become channels of domestic sullage from the unserviced informal settlements. These

polluting outfalls have made the water quality of Motoine to deteriorate further as it flows

through Kibera into the Nairobi Dam. Eutrophication of the Nairobi Dam is largely responsible

for the water hyacinth infestation. The Motoine leaves the Nairobi Dam as the Ngong River at

the spillway, and data shows that it undergoes some natural purification process as it cascades

through the concrete channel in the South C area and the Industrial Area. This situation does

not persist for long, as more serious forms of pollution are released into the Ngong' River in the

Industrial Area (Ndede, 2002).

2.4.3 The MPU section Downstream of the Nairobi dam and starting with the weir, the river channel is for the most

part channelised as it flows through the Industrial area. The stream in this section is fast

flowing and much of the riparian vegetation has been removed resulting in the exposure of the

river to heavy pollution from runoff. The channels are usually wide and shallow with concrete

lining, and several bridges cross over the river beneath which a lot of garbage is dumped. The

distance covered by this section is quite short, between 2 and 4 km (Kahara, 2002).

Activities along the channelised river section

Just before the Industrial area, the river passes through another CPU section (Mukuru slum),

where various activities such as several small scale industries have been set up along the river

bank. Car garages and other large industries have been observed discharging their effluent

directly into the river. Pollutants ranging from automobile oils, pigments detergents and

unidentified solid material are found emerging from broken sewer lines. As the river departs

the MPU section, the discharge rises quite steeply and it has been suggested that this is a result

of direct sewage input.


24 E25-0177/05

The section downstream of the confluence with the Nairobi River is characterized by low

human activity apart from some agriculture towards it’s confluence with the Athi river. Several

tributaries join the river at this stage and the Dandora Sewage Treatment Works also discharge

treated sewage into the waters.

2.5 Hydrological measurements

Proper interpretation of the import of water quality variables in a sample taken from a river

requires information of the discharge of the river at the time and place of sampling. In order to

calculate the mass flux of chemicals in the water, (the mass of a chemical variable passing a

cross-section of the river in a unit time), a time series of discharge measurement is critical.

The flow rate or discharge of a river is the volume of water flowing through a cross section in a

unit of time and is usually expressed as m3/s. It is calculated as the product of average velocity

and cross-sectional area but is affected by hydraulic variables such as water depth, alignment

of the channel, gradients and roughness of the river bed. Discharge may be estimated by the

slope-area method, using hydraulic variables in one of the variations of the Manning’s equation

which, although developed for conditions of uniform flow in open channels, may give an

adequate estimate of the non-uniform flow which is usual in natural channels.

Velocity usually varies as a parabola from zero at the channel bottom to a maximum near the

surface. It has been determined empirically that for most channels the velocity at six-tenths of

the total depth below the surface is a close approximation to the mean velocity at the vertical

line. However, the average of the velocities at two-tenths and eight-tenths depth below the

surface on the same vertical line provides a more accurate value of mean velocity at that

vertical line. Velocities also vary across the channel, and measurements must therefore be

made at several points across the channel. The depth of the river varies across the width, so the

cross-section is divided into a number of vertical sections. No section should include more than

10-20% of the total discharge. Thus, between 5 and10 vertical sections are used.

A distribution of the measured velocity with depth of the river is shown in the diagram on the

following page.


25 E25-0177/05

0.2d

0.6d

0.8d

vmean

Diagram 2.2: A distribution of the measured velocity with depth of the river. (Source: pg 306,

E. Kuusisto “Water quality monitoring- a practical guide to design and implementation of

freshwater quality studies and monitoring programmes; chapter 7: physical and chemical

analysis”. 1996)

2.6 Nutrients in the Ngong River An increasing level of nutrients has been released onto our rivers which has been largely

responsible for eutrophication occurring in running waters since the 1970’s (Bartram, 1997).

These nutrients are discussed in the section that follows.

2.6.1 Nitrogen compounds Nitrogen is important in living organisms as an important component of proteins, including

genetic material. Plants and micro-organisms convert inorganic nitrogen to organic forms. In

the environment, inorganic nitrogen occurs in a range of oxidation states as nitrate (NO3-) and

nitrite (NO2-), the ammonium ion (NH4

+) and the molecular (N2). It undergoes biological and

non-biological transformations in the environment as part of the nitrogen cycle. The major

non-biological processes involve phase transformations such as volatisation, sorption and

sedimentation. The biological transformations consist of:

a) Absorption of inorganic forms (ammonium and nitrate) by plants and micro-

organisms to form organic nitrogen e.g. amino acids

b) Reduction of nitrogen gas to ammonia and organic nitrogen by micro-organisms

c) Complex heterotrophic conversions from one organism to another

d) Oxidation of ammonia to nitrate and nitrite (nitrification)

e) Ammonification of organic nitrogen to produce ammonia during the decomposition of

organic matter

f) Bacterial reduction of nitrate to nitrous oxide (N2O) and molecular nitrogen (N2)

under anoxic conditions (denitrification).


26 E25-0177/05

2.6.1.1 Ammonia Ammonia occurs naturally in water bodies arising from the breakdown of nitrogenous organic

and inorganic matter in soil and water, excretion by biota, reduction of the nitrogen gas in

water by micro-organisms and from gas exchange with the atmosphere. It is also discharged

into water bodies by some industrial processes (e.g. ammonia-based pulp and paper

production) and also as a component of municipal or community waste. At certain pH levels,

high concentrations of ammonia are toxic to aquatic life and therefore detrimental to the

ecological balance of water bodies (Bartram, 1997).

In aqueous solution, un-ionised ammonia exists in equilibrium with the ammonium ion. Total

ammonia is the sum of these 2 forms. Ammonia also forms complexes with several metal ions

and may be absorbed onto colloidal particles, suspended sediments and bed sediments. It may

also be exchanged between sediments and the overlying water. The concentration of un-ionised

ammonia is dependent on the temperature, pH and total ammonia concentration.

The change in percentage of the 2 forms at different pH values showing the relationship

between the percentages of un-ionised ammonia and varying pH in freshwater is shown in the

diagram that follows:


27 E25-0177/05

Diagram 2.3: the relationship between percentage un-ionised ammonia and varying pH in

freshwater. (Source: pg 78, D. Chapman and V. Kimstach, “Water Quality Assessments- A

Guide To The Use Of Biota, Sediments And Water In Environmental Monitoring”, 1997.)

Unpolluted waters contain small amounts of ammonia and ammonia compounds, usually 0.1

mg/l as nitrogen. Total ammonia concentrations measured in surface waters are typically less

than 0.2 mg/l N but may reach 2-3 mg/l N. Higher concentrations could be an indication of

organic pollution such as from domestic sewage, industrial waste and fertiliser run-off.

Ammonia is, therefore, a useful indicator of organic pollution. Natural seasonal fluctuations

also occur as a result of the death and decay of aquatic organisms, particularly phytoplankton

and bacteria in nutritionally rich waters, high ammonia concentrations may also be found in the

bottom waters of lakes which have become anoxic. (Chapman, 1997)

Samples for the analysis of ammonia should be analysed within 24 hours. If this is not possible

the sample can be deep frozen or preserved with 0.8 ml of sulphuric acid for each litre of

sample and then stored at 4⁰C. Prior to analysis any acid used as a preservative should be

neutralised. There are many methods available for measuring ammonia ions. The simplest,

which are suitable for waters with little or no pollution, are colorimetric methods using

Nessler’s reagent or the phenate method. For high concentrations of ammonia, such as occur in

0

20

40

60

80

100

120

4 5 6 7 8 9 10 11 12

% of total ammon

ia

pH

Relationship between percentage un‐ionised ammonia and varying pH in

freshwater

30degrees

15degrees

0degrees


28 E25-0177/05

wastewaters, a distillation and titration method is more appropriate. Total ammonia nitrogen is

also determined as part of the Kjedahl method (Ballance, 1996). This method of analysis is

described in the subsequent chapter.

2.6.1.2 Nitrate and Nitrite The nitrate ion is the common form of combined nitrogen found in natural waters. It may be

biochemically reduced to nitrite by denitrification processes, usually under anaerobic

conditions. The nitrite ion is rapidly oxidised to nitrate. Natural sources of nitrate to surface

waters include igneous rocks, land drainage and plant and animal debris. Nitrate is an essential

nutrient for aquatic plants and seasonal fluctuations of nitrates in water can be caused by plant

growth and decay. Natural concentrations, which seldom exceed 0.1 mg/l NO3—N, may be

enhanced by municipal and industrial wastewaters, including leachates from waste disposal

sites and sanitary landfills. In rural and suburban areas, the use of inorganic nitrate fertilisers

can be a significant source (D. Chapman, 1997).

When influenced by human activities, surface waters can have nitrate concentrations up to 5

mg/l NO3—N but often less than 1 mg/l NO3

—N. Concentrations in excess of 5mg/l NO3—N

usually indicate pollution by human and animal waste, or fertiliser run-off. In cases of extreme

pollution, concentrations may reach 200mg/l NO3—N. The World Health Organisation (WHO)

recommended maximum limit for NO3 in drinking water is 50 mg/l and waters with higher

concentrations can represent a significant health risk. (WHO 1984, Guidelines for drinking

water quality. Volume 2)

Nitrate occurs naturally in groundwater as a result of soil leaching but in areas of high nitrogen

fertiliser application it may reach very high concentrations (approximately 500 mg/l NO3—N).

In some areas, sharp concentrations in ground waters over the last 20 or 30 years have been

related to increased fertiliser applications (Hagebro et al., 1983; Roberts et al., 1987). Increased

fertiliser use is however not the only source of nitrate leaching into groundwater. Nitrate

leaching from unfertilised grassland or natural vegetation is normally minimal, although soils

in such areas contain sufficient organic matter to be a large potential source of nitrate. On

clearing and ploughing for cultivation, the increased soil aeration that occurs enhances the

action of nitrifying bacteria, and the production of soil nitrate.

Nitrite concentrations in freshwaters are usually very low, 0.001 mg/l NO2-N, are rarely higher

than 1 mg/l NO2-N. High nitrite concentrations are generally indicative of industrial effluent

and are often associated with unsatisfactory microbiological quality of water. (Hem, 1989).


29 E25-0177/05

Determination of nitrate plus nitrite in surface waters gives a general indication of the nutrient

status and level of organic pollution. Consequently, these specimens are included in most basic

water quality surveys and multipurpose or background monitoring programmes. As a result of

the potential health risk in high levels of nitrate, it is also measured in drinking water sources.

However, as little nitrate is removed during the normal processes for drinking water treatment.

Samples taken for the determination of nitrate and nitrite should be collected in glass or

polythene bottles and filtered and analysed immediately. If this is not possible, 2-4 ml of

chloroform per litre can be added to the sample to retard bacterial decomposition. The sample

can then be cooled and stored at 3-4 ⁰C. As determination of nitrate is difficult, due to

interferences from other substances present in the water, the precise choice of method may

vary according to the concentration of nitrate as N. Alternatively one sample can be analysed

for total nitrogen and the other for nitrite, and the nitrate concentration obtained from the

difference between the 2 values. Nitrite concentrations can be determined using

spectrophotometric methods. (Ballance, 1996).

2.6.1.3 Organic Nitrogen

Organic nitrogen consists mainly of protein substances (e.g. amino acids, nucleic acids and

urine) and the product of their biochemical decomposition transformations (e.g. humic acids

and fulvic acids). Organic nitrogen is naturally subject to the seasonal fluctuations of the

biological community because it is mainly formed in water by phytoplankton and bacteria, and

cycled within the food chain. Increased concentrations of organic nitrogen could be an

indication of pollution of the water body.

Organic nitrogen is usually determined using the Kjedahl method which gives total ammonia

nitrogen plus total organic nitrogen. The difference between the total nitrogen and the

inorganic forms gives the total organic nitrogen content. Samples must be unfiltered and

analysed within 24 hours, since organic nitrogen is rapidly converted to ammonia. This process

can be retarded if necessary by the addition of 2-4 ml of chloroform or 0.8 ml of concentrated

H2SO4 per litre of sample. Storage should be at 2-4 ⁰C, and when this is necessary, the

condition and duration of preservation should be stated with the results (Ballance et al., 1996).

Photochemical methods can also be used in place of Kjedahl method. These methods oxidise

all organic nitrogen (as well as ammonia) to nitrates and nitrites and, therefore, the

measurements of these must already have been carried out on the sample beforehand. If

samples are filtered total dissolved nitrogen is determined instead of the total organic nitrogen.


30 E25-0177/05

2.6.2 Phosphorus compounds

Phosphorus is an essential nutrient for living organisms and exists in water bodies as both

dissolved and particulate species. It is generally the limiting nutrient for algal growth and,

therefore, controls the primary productivity of a water body. Artificial increases in

concentrations due to human activities are the principal cause of eutrophication (Chapman,

1997).

In natural waters and in wastewaters, phosphorus occurs mostly as dissolved orthophosphates

and polyphosphates, and organically bound phosphates (Kimstach, 1997). Changes between

these forms occur continuously due to decomposition and synthesis of organically bound forms

of phosphate that occur at different pH values in pure water is shown in the diagram below:

Diagram 2.4: forms of phosphate that occur at different pH values in pure water. (Source:

page 85, D. Chapman and V. Kimstach, “Water Quality Assessments- A Guide To The Use Of

Biota, Sediments And Water In Environmental Monitoring”, 1997.)

It is recommended that phosphate concentrations are expressed as phosphorus, ie mg/l PO4-P

(and not as mg/l PO43-.

0

20

40

60

80

100

120

4 5 6 7 8 9 10 11 12

% of total pho

spha

tes

pH

Equilibrium of different forms of phosphates in relation to pH

H2PO4‐

HPO42‐

PO43‐

H2PO4‐

HPO4


31 E25-0177/05

Natural sources of phosphorus are mainly the weathering of phosphorus-bearing rocks and the

decomposition of organic matter (particularly those containing detergents), industrial effluents

and fertiliser run-off contribute to elevated levels in surface waters. Phosphorus associated with

organic and mineral constituents of sediments in water bodies can also be mobilised by

bacteria and released to the water column.

Phosphorus is rarely found in high concentrations in freshwaters as it is actively taken up by

plants. As a result there can be considerable seasonal fluctuations of phosphorus concentrations

in surface waters. In most natural surface waters, phosphorus ranges from 0.005 to 0.020 mg/l

PO4-P concentrations. As low as 0.001 mg/l PO4-P may be found in some enclosed saline

waters. Average groundwater levels are about 0.02 mg/l PO4-P (Hem, 1989).

High concentrations of phosphates can indicate the presence of pollution and are largely

responsible for eutrophic conditions.

Phosphorus concentrations are usually determined as orthophosphates, total inorganic

phosphate or total phosphorus (organically combined phosphorus and all phosphates). The

dissolved forms of phosphorus are measured after filtering the sample through a pre-washed

0.45 m pore diameter membrane filter. Particulate concentrations can be deduced by the

difference between total and dissolved concentrations. Phosphorus is readily absorbed onto the

surface of the sample containers and, therefore, containers should be rinsed thoroughly with the

sample before use. Samples for phosphate analysis can be preserved with chloroform and

stored at 2-4 ⁰C for up to 24 hours. Samples for total phosphorus determinations can be stored

in a glass flask with a tightly fitting glass stopper, provided 1 ml of 30% sulphuric acid is

added per 100 ml sample.

2.7 Results from previous studies and the gap that exists

A number of previous studies have been done to determine the quality of water of the Ngong

River. Among these, the most notable one is the Nairobi River Basin Project (NRBP) which

was initiated in 1999 to address pollution problems of the Nairobi Rivers. The Phase II of the

NRBP was conducted on the Ngong' /Motoine-Nairobi River to provide information and to

identify the major point sources of pollution. In the water quality assessment the longitudinal

profile of the Ngong' River was represented by 20 sample stations. The sample stations were as

shown in Table 2 that follows:


32 E25-0177/05

Table 2.1: station positions for NRDP UON/UNEP project (Feb. to Nov. 2003.)

STATION NUMBER STATION POSITION DISTANCE (km)

1 Motoine Dam 0.0

2 Ngong Rd bridge 2.5

3 Jamhuri Dam outlet 5.8

4 Ngong River 5.8

5 Kibera bridge 7.9

6 Inlet to Nairobi Dam 9.6

7 Midpoint of the dam 10.3

8 Weir 10.8

9 Langata Rd bridge 1 11.9

10 Langata Rd bridge 2 12.0

11 Mombasa Rd bridge 12.7

12 KCB bridge 14.2

13 Enterprise Rd bridge 15.1

14 Outer-ring rd bridge 17.8

15 Kangundo Rd bridge 25.4

16 Nairobi river confluence 27.1

17 Dandora sewage treatment

plant

28.7

18 After kamiti river confluence 32.3

19 After Nairobi falls 35.2

20 After ruiru confluence 42.3

In this current project, the scope of the study limits the section of the Ngong’ River to be

considered and the following sample stations were chosen:


33 E25-0177/05

Table 2.2: sampling stations selected for this experiment.

OLD STATION NUMBER NEW STATION

NUMBER

STATION POSITION

2 1 Ngong Rd bridge

3 2 Jamhuri Dam outlet

5 3 Kibera bridge

8 4 Weir

13 5 Enterprise Rd bridge

14 6 Outer-ring rd bridge

In order to facilitate a comparative analysis of the results obtained from assessment to the

pollution levels, the sample sites taken for this study coincide as far as possible with the

sampling stations for the Phase II of the NRBP, UoN-UNEP project. These include; Kariani

Dam on Ngong' Road, the outlet of Jamhuri park Dam, the Kibera bridge, the weir/outlet of

Nairobi Dam, Dunga Road bridge, and the outer ring road bridge.

The results from Phase II of the NRBP, UoN-UNEP project showed that the nutrients levels in

the Ngong River varied considerably. These results are shown in appendix 9.4.

At the upstream section, the concentration of phosphate was relatively low, but enough to

support excessive growth of the water plants at the stagnant sections of the river. The nitrite

levels were low varying from 0 to 0.3 mg/l. Ammonia concentration was also low at the

upstream section of the river, indicating absence of human waste contamination.

The ammonia concentration in the upstream was below 2 mg/l. However, the concentration

increased to 35 mg/l at the Kibera bridge. This was an indication of the presence of human

waste in the river. These high levels of ammonia was supported by the observation that several

toilets had been erected over the river. The free ammonia water was an indication of presence

of fresh sewage in the river. At the inlet to Nairobi dam, the ammonia concentration was 40

mg/l (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report).

The water leaving the Nairobi dam showed free ammonia of 33mg/l. This was indication of

anoxic conditions. Wetlands are also major sources of ammonia arising from anaerobic

decomposition of organic matter. The concentration of free ammonia remained high at the

stations downstream, an indication of discharge of wastes high in free ammonia such as

domestic sewage and industrial discharges as well as existence of anoxic conditions in the

river. These would give rise to anaerobic breakdown of organic matter where ammonia is one

of the by-products. (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report.)


34 E25-0177/05

During the wet weather, the free ammonia concentration in the river was 40 mg/l and below.

The cause of reduced levels of ammonia was dilution from surface runoff. However, whereas

ammonia concentration was low in the upstream stations averaging 0.4 mg/l, downstream, at

the industrial areas stations, the ammonia concentrations was between 26 – 28 mg/l. The major

source of free ammonia in the river was thus concluded to be due to discharge of human waste

mainly from the informal settlements (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003,

Final report.)

The distribution of the nutrient pollutants were plotted into diagrams (appendix 9.3 and 9.4).

At the inlet of the Nairobi dam phosphate recorded a high of 2.5 mg/l, indicating contribution

from the Kibera informal settlement. Downstream of the Nairobi Dam up to the Mombasa

Road Bridge, phosphate concentration was between 0.1 mg/l and 0.2 mg/l, high enough to

cause high plant productivity in the river. Stations in the industrial area registered high levels

of phosphate of between 1.9 – 2.7 mg/l (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003,

Final report.) The major source of phosphate pollution was taken to be from the informal

settlements, with other sources being industries.

The Final report from Phase II of the NRDP, UON/UNEP project, Feb- Nov 2003, thus

concluded that the natural sources of nutrient pollution included animal and human waste

sources. The animal sources include domestic waste in the form of compounds containing

nitrogen and phosphorus in free and combined form. The nitrogen and phosphorus combined in

waste products undergo decomposition to release nitrogen and phosphorus usually as oxides of

these elements. These oxides are subsequently sources of nutrients for plant growth.

The anthropogenic sources include surface run-off from agricultural land application and run-

off from factories producing or handling fertilizer products.

The excessive plant growth in the Ngong River also hinders flow of water resulting in stagnant

pools of water and reduced light transmittance and hence reduced dissolved oxygen exchange

from air to river water (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report).

The management of organic pollution from domestic and industrial sources and farming

activities within the riparian way-leave will go a long way in reducing nutrients and hence

restoration of river ecological balanced flora and fauna.

Among the recommendations that were made in relation to the report, the following directly

address the presence of the nutrient pollutants in the Ngong River:


35 E25-0177/05

a) The discharge of human waste into the river should be addressed through efforts to

have human settlements and agricultural activities within the river relocated or

stopped.

b) The industrial discharges should be stopped through efforts by the industries to take

measures to address pollution emanating from their production processes.

c) Continue to build the capacity of the Local Authorities through improvement of the

monitoring laboratories and equipment as well as organizing refresher courses

Despite the strides made to analyse the water quality, there has been little attempt to model the

water quality of the Ngong' River as a support system tool for water pollution control as well as

the assessment of the effects of point and diffuse pollution.


36 E25-0177/05

3. RESEARCH METHODOLOGY The process of water quality modelling generally consists of the following steps (Himesh,

2000):

1. Orientation/ Problem identification

2. Formulation of relations between variables and parameters

3. Non dimensionalization

4. Solution of model equations

5. Preliminary test application

6. Model Verification

7. Reiteration of steps 2 – 6

8. Implementation

A modelling flow chart is shown in the diagram below:

Diagram 3.1: Modelling flow chart (adapted from Thomann and Mueller, 1987 and Chapra,

1997).


37 E25-0177/05

The steps are discussed below:

3.1 Orientation The modelling process always starts with an orientation stage, in which the modeller gets

aquainted with the system under consideration, that is, the Ngong' River. This is done by

means of observations and information from experts and literature. This stage also involves the

identification of the relevant variables and parameters that the system is meant to use to

interpret the system (Erturk et. al, 2004).

The variables include:

• the distance along the reach [L]= x

• the cross-section area [L2]= A(x)

• the flow rate along the reach [L3·T-1]= Q(x)

• the concentration of the relevant water quality variable [M·L-3]= C(x)

3.1.1 Data collection methods Data collection methods are the various techniques that are employed in order to carry out a

water quality assessment. They involve the measurement of the water discharge at the

sampling points as well as the collection and analysis of samples.

3.1.2 Measuring stream flow: The discharge is the volume flowing per unit period of time (Chapman, 1996).Discharge

should be measured at the time of sampling. An estimate method to determine discharge is to

measure the cross-sectional area of the stream and then getting a rough estimate of the velocity

of the river by measuring the time it takes a weighted float to travel fixed distance along the

stream.

For best results, the cross section of the stream at the point of measurement should have the

following ideal characteristics (Kuusisto, 1996):

• The velocities at all the points are parallel to one another and at right angles to the

cross section of the stream.

• The curves of distribution of velocity in the section are regular in the horizontal and

vertical planes

• The cross-section should be located at a point where the stream is nominally straight

for at least 50 m above and below the measuring station

• The velocities are greater than 10 to 15 cm/s

• The bed of the channel is regular and stable


38 E25-0177/05

• The depth of flow is greater than 30 cm

• The stream does not overflow its banks

• There is no aquatic growth in the channel

It is rare for all these characteristics to be present at any one measuring station and

compromises usually have to be made.

Procedure:

1. All measurements of distance should be made to the nearest centimetre

2. Measure the horizontal distance b1, from reference point from reference point 0 on

shore to the point where the water meets the shore.

3. Measure the horizontal distance b2 from reference point 0 to vertical line 2

4. Measure the channel depth d2 at the vertical line 2

5. Make measurements to determine the mean velocity v2 at vertical line 2.

6. Repeat steps 3-5 at all vertical lines across the width.

The method described in the procedure above is illustrated in the diagram on the next page. It demonstrates the readings that are required for the determination of the cross-sectional area.

b4

b3

b2

b1

d2 d3 d4

Diagram 3.1 (Source: pg 307, E. Kuusisto “Water quality monitoring- a practical guide to

design and implementation of freshwater quality studies and monitoring programmes; chapter

7: physical and chemical analysis”. 1996)

The computation of discharge is based on the assumption that the average velocity measured at

a vertical line is valid for a rectangle that extends half of the distance to the verticals on each

side of it, as well as throughout the depth of the vertical. Thus the mean velocity, v1’ would

apply to a rectangle bounded by the dashed vertical lines.


39 E25-0177/05

e.g. the area of a first rectangle is:

a1= *d2 Equation 3.1

Where: a1 = the area of the 1st rectangle of the cross-section

b2 = the horizontal distance to the 2nd point on the cross-section

d2 = depth of the river at the 2nd point of the cross-section

and the discharge through it will be:

Q1= a1*v1’ Equation 3.2

Where: v1’ = velocity of the river at the cross-section

The same procedure is repeated for the other rectangles. The discharge for the whole cross-

section will be:

Qt=Q1+Q2+......+QN = Qx Equation 3.3

Where: Qt = total discharge through the cross-section

Qx = discharge through each rectangle making up the cross-section area.

3.1.3 Sampling: Type of sampling that was employed in this study is grab sampling at selected depth and time.

Selection of sampling points is based on the following criteria (Ballance, 1996):

• To avoid polluted water at major outfalls such as drains, industrial outfalls, stagnant

pools and areas of standing water.

• To determine the effects of the dam and any standing water bodies along the stream

• Accessibility to sampling points.

• Cost effectiveness for collection, analysis and reporting

• To obtain spatially (along the profile of the river), hydrologically and temporally

distributed samples.

• Sustainability of the analysis system.

3.1.4 Materials and methods of water quality analysis For this particular study, the materials and methods that are considered are for the

determination of the nutrient pollution levels in the Ngong' River. Standard methods are used

for the analysis of nutrients.


40 E25-0177/05

3.1.4.1 BOD Standard methods were used to determine BOD. BOD is used as an approximate measure of

the amount of biochemically degradable organic matter present in a sample. The 5-day

incubation period has been accepted as the standard for this test (ISO 1990 – Water Quality).

BOD is useful for determining the relative waste loadings.

Sample handling

The test should be carried out as soon as possible after samples have been taken. If samples are

kept at room temperature for several hours, the BOD may change significantly, depending on

the character of the samples. Samples must be free from all added preservatives and stored in

glass bottles (Chapman D. 1997).

Interferences

If the pH of the sample is not between 6.5 and 8.5, add sufficient alkali or acid to bring it

within that range. Determine the amount of acid and alkali to be added by neutralising separate

portion of the sample to about pH 7.0 with 1 mol/l solution of the alkali or acid (Chapman D.

1997).

Apparatus

1. BOD bottles

2. Incubator

3. Beakers

4. pipette

Reagents

1. Phosphate buffer (dissolve 8.5g K2PO4, 21.75g K2HPO4, 33.4g Na2HPO4. 7H2O and

1.7g NH4Cl in 500ml of distilled water and dilute to 1 litre).

2. Magnesium sulphate solution (dissolve 22.5g Mg SO4.7H2O in distilled water and

dilute to 1 litre).

3. Calcium chloride solution (dissolve 27.5g CaCl2 in distilled water and dilute to 1

litre).

4. Ferric Chloride solution (dissolve 0.25g FeCl3.6H2O in distilled water and dilute to 1

litre)

Procedure

1. Into 1000ml distilled water, add 1 ml of each of the above reagents.

2. Aerate the above solution till the DO is at least 8mg/l.


41 E25-0177/05

3. Into a 250ml beaker, add the sample in 5, 10, 15 and 20ml amounts, making one as a

blank of only aerated water.

4. Make up to 200ml using the aerated water.

5. Measure the DO of each of the five solutions so made.

6. Transfer the solution above into the BOD bottle ensuring that there is no air trapped.

7. Cover with cellophane

8. Incubate at 20⁰ C for 5 days.

9. Measure the DO5.

Calculation

BOD, mg/l = Equation 3.4

Where:

D1 = DO of the diluted sample immediately after preparation, mg/l

D2 = DO of the diluted sample after 5 days incubation at 20⁰ C

P = decimal volumetric fraction of sample used.

3.1.4.2 Nitrogen ammonia: Palintest method was used to determine Nitrogen Ammonia. When nitrogen organic matter is

destroyed by microbiological activity, ammonia is produced and is therefore found in many

surface and ground waters. Higher concentrations occur in water polluted by sewage,

fertilisers, agricultural wastes or industrial wastes containing organic nitrogen, free ammonia or

ammonium salts (Ballance, 1997).

Certain aerobic bacteria convert ammonia into nitrites then nitrates. Nitrogen compounds, as

nutrients for aquatic micro-organisms, may be partially responsible for the eutrophication of

lakes and rivers. Ammonia can result from the natural reduction processes under anaerobic

conditions.

Sample handling

It is not possible to carry out the determination very soon after sampling, the sample should be

refrigerated at 4 degrees C. Chemical preservation may be achieved by adding either 20 -40 mg

HgCl2 or 1 ml H2SO4 to 1 litre of sample.


42 E25-0177/05

Principle

Ammonia can be quantitatively recovered from a sample by distillation under alkaline

conditions into a solution of boric acid followed by titration with standard acid. The method is

particularly suitable for the analysis of polluted surface water contain sufficient ammonia to

neutralise at least 1 ml of 0.00714 mol 1-1 HCl.

Apparatus

1. 1 litre round bottomed heat-resistant glass flask fitted with a splash head

2. Palintest Photometer at 640nm.

3. Usual laboratory glassware

Reagents

1. Palintest Ammonia No. 1 tablets.

2. Palintest Ammonia No. 2 tablets.

Procedure

1. Fill test tube with sample to the 10ml mark.

2. Add ammonia No. 1 and one ammonia No. 2 tablet, crush and mix to dissolve.

3. Stand for 10 minutes to allow colour development.

4. Select wavelength 640 nm on the photometer.

5. Take photometer reading (%T) in usual manner.

6. Consult ammonia calibration Diagram to find ammonia concentration.

Calculation

Ammonia nitrogen (as N) = ammonia reading in mg/1N.

The ammonia calibration table is shown below and is used for the determination of ammonia

concentration from the absorbance readings from the testing of the samples collected.


43 E25-0177/05

Table3.1: the ammonia calibration Diagram.

%T 9 8 7 6 5 4 3 2 1 0

80 - - - - - 0.00 0.00 0.01 0.01 0.02

70 0.02 0.03 0.04 0.04 0.04 0.05 0.05 0.06 0.06 0.07

60 0.07 0.08 0.09 0.09 0.10 0.11 0.11 0.12 0.13 0.13

50 0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.20 .020 0.21

40 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.30 0.31

30 0.32 0.33 0.34 0.36 0.37 0.38 0.39 0.41 0.42 0.44

20 0.45 0.47 0.48 0.50 0.51 0.53 0.55 0.57 0.59 0.61

10 0.63 0.66 0.68 0.71 0.74 0.77 0.80 0.83 0.87 0.91

0 0.96 1.00 - - - - - - - -

3.1.4.3 Nitrogen, nitrate: Nitrate, the most highly oxidised form of the nitrogen compounds, is commonly present in

surface water because it is the end product of organic nitrogenous matter. Significant sources

of nitrate are chemical fertilisers from cultivated land and drainage from livestock feedlots, as

well as domestic and some industrial waste (Chapman D. 1997).

The determination of nitrate helps the assessment of the character and degree of oxidation in

the surface water in biological processes and in the advanced treatment of wastewater.

Unpolluted natural waters usually contain only minute amounts of nitrate. In surface water,

nitrate is a nutrient taken up by plants and assimilated into cell protein (Ballance, 1997).

Stimulation of plant growth, especially of algae, may cause water quality problems associated

with eutrophication. The subsequent death and decay of algae produces secondary effects on

the water quality, which may also be undesirable. High concentrations of nitrate in drinking

water may present a risk to bottled-fed babies under 3 months of age because the low acidity of

their stomachs favours the reduction of nitrates into nitrites by microbial action (Chapman D.

1997). Nitrite is readily absorbed into the blood where it combines irreversibly with

haemoglobin to form methaemaoglobin, which is ineffective as an oxygen carrier in the blood.

The determination of nitrate in water is difficult because of interferences and much more

difficult in wastewaters because of higher concentrations of numerous interfering substances.


44 E25-0177/05

Sample handling

To prevent any change in the nitrogen balance through biological activity, the nitrate

determination should be started as soon as possible after sampling. If storage is necessary,

samples should be kept at a temperature just above freezing point, with or without

preservatives, such as 0.8ml of concentrated sulphuric acid. If acid preservation is used then

the sample should be neutralised to about pH 7 immediately before the analysis is begun

(Chapman D. 1997).

The method used for the determination of nitrate level is the Phenol Disulphonic Acid method.

Principle

Nitrate in contact with sulphonic acid produces nitric acid which in dry condition (in presence

of excess concentrated H2SO4) brings about nitration of phenol disulphonic acid. This

nitrophenolic product gives intense yellow colour in alkaline medium which is measured

through colorimeter (the reaction must proceed in cold otherwise nitric acid may be lost by

volatization.)

Apparatus

1. Spectrometer

2. Hot water bath or similar apparatus

Reagents

1. Phenol disulphonic acid: dissolve 25 g of white phenol in 150 mL of concentrated

sulphuric acid then again add 85 mL of concentrated sulphuric acid. Heat it for 2 hrs

in a hot water bath, cool and keep the solution in a dark bottle.

2. Liquor ammonia(LR grade): it is diluted with equal volume of water.

3. Standard nitrate solutions: dissolve 0.722 g of anhydrous potassium nitrate in destilled

water to prepare 1 L of stock solution. This stock solution contains 100 mg NO3-N/L

(or 443 mg NO3 ions/L).

Procedure

1. Take 25 mL of sample in a porcelain dish (50 mL capacity) and evaporate it to

dryness on a hot water bath (if porcelain dish is not available take 50 mL beaker or

silica dish).

2. Add 3 mL of phenol disulphonic acid to the residue and dissolve the latter by rotating

the dish.


45 E25-0177/05

3. After 10 min, 15 mL of distilled water is added and stirred with a glass rod. On

cooling, the contents are washed down into 100 mL volumetric flask.

4. Add p. nitrophenal indicator

5. Add ammonia (1:1) slowly with mixing till the solution is alkaline as indicated by the

development of yellow color due to the presence of nitrate. Then add another 2 mL of

ammonia and the volume made up (100 mL) with distilled water.

6. Intensity of yellow colour is read in the colorimeter at 420nm (blue filter).

Observations

Preparation of standard curve for nitrate

A stock solution containing 100 ppm nitrate nitrogen (NO3-) is prepared by dissolving 0.7215 g

of AR grade potassium nitrate (oven-dried and cooled) in distilled water and making the

volume to one litre. This is diluted ten times to give a 10 ppm NO3- solution. Aliquots (2, 5,

10, 15, 20, 25 mL) are evaporated on boiling water bath to dryness in small porcelain dishes

(or beakers). When cool, 3 mL of phenol disulphonic acid is added and the yellow colour is

developed and read as described above. A blank (without nitrate) must be run and correction

made by adjusting the colorimeter to zero with blank. A calibration curve is drawn between

concentration of NO3- and colorimeter reading.

Calculation

Mg of NO3-/L =

Equation 3.5

3.1.4.4 Nitrogen, Nitrite Palintest method was used for the determination of nitrite concentration in the samples. Nitrite

is an unstable, intermediate stage in the nitrogen cycle and is formed in water either by the

oxidation of ammonia or by the reduction of nitrate (Ballance, 1997). Thus, biochemical

processes can cause a rapid change in the nitrite concentration in a water sample. In natural

waters nitrite is normally present only in low concentrations (a few tenths of an mg per litre).

Higher concentrations may be present in sewage and industrial wastes, in treated sewage

effluents and in polluted waters.

Sample handling

The determination should be made promptly on fresh samples to prevent bacterial conversion

of the nitrite to nitrate or ammonia. In no case should acid preservation be used for samples to


46 E25-0177/05

be analysed for nitrite. Short-term preservation for 1 to 2 days is possible by the addition of 40

mg mercuric ion as HgCl2 per litre of sample, with storage at 4⁰C (Ballance, 1997).

Interferences

There are very few known interferences as concentrations less than 1000 times that of nitrite.

However, the presence of strong oxidants or reductants in the samples will readily affect the

nitrite concentrations. High alkalinity (>600 mg/l as CaCo3) will give low results owing to a

shift in pH.

Apparatus

1. Palintest photometer

2. Round test tubes 10ml glass

Reagents

1. Palintest nitrocol tablets

Procedure

1. Fill round test tube with sample to the 10ml mark.

2. Add one nitrocol tablet, crush and mix to dissolve.

3. Select wavelength 520nm on the photometer.

4. Take photometer reading in usual manner

5. Consult nitrocol calibration Diagram to find nitrite concentration in the sample

The nitrocol calibration table for the determination of nitrite concentrations from the

absorbance of the sample is shown in the page that follows.


47 E25-0177/05

Table 3.3: nitrocol calibration Diagram for the determination of nitrite concentrations.

%T 9 8 7 6 5 4 3 2 1 0

90 0 .001 .003 .004 .006 .007 .009 .011 .012 .014

80 .016 .018 .019 .021 .023 .025 .027 .028 .030 .032

70 .034 .036 .038 .040 .042 .044 .046 .048 .051 .053

60 .055 .057 .060 .062 .064 .067 .069 .072 .074 .077

50 .079 .082 .084 .087 .090 .093 .096 .099 .102 .105

40 .108 .111 .114 .118 .121 .124 .128 .132 .135 .139

30 .143 .147 .151 .155 .160 .164 .169 .173 .178 .183

20 .189 .194 .200 .205 .212 .218 .224 .231 .238 .246

10 .254 .262 .271 .280 .290 .301 .312 .325 .338 .353

0 .369 .408 .431 .460 .5 - - - - -

Calculation

Read the concentrations of NO2-N in samples directly from the calibration curve. If less than

50 ml of the sample is taken, calculate the concentrations as follows:

Nitrite nitrogen (as N) =

mg/l Equation 3.6

3.1.4.5 Phosphates Phosphorus occurs in natural waters and wastewaters almost solely as phosphates. Phosphates

are presently in fertilisers and in many detergents. Consequently, they are carried into both

ground and surface waters with sewerage, industrial wastes and storm run-off (Ballance, 1997).

High concentrations of phosphorus compounds may produce a secondary problem in water

bodies where algal growth is normally limited by phosphorus. In such situations the presence

of additional phosphorus compounds can stimulate algal productivity and enhance

eutrophication processes.

Phosphates that respond to colorimetric tests without preliminary hydrolosis or oxidative

digestion of the sample are termed “reactive phosphorus.”

Principle

Organically combined phosphorus and all phosphates are first converted to orthophosphate. To

release phosphorus from combination with organic matter, a digestion or wet oxidation


48 E25-0177/05

technique is necessary. The least tedious method, wet oxidation with potassium

peroxydisulphate, is recommended.

Orthophosphate reacts with ammonium molybdate to form molybdophosphoric acid. This is

transformed by the reductants to the intensely coloured complex known as molybdenum blue.

The method based on reduction with ascorbic acid is preferable. Addition of potassium

antimonyl tartrate increases the coloration and the reaction velocity at room temperature.

For concentrations of phosphate below 20 g/l, the recommended procedure involves

extraction of the molybdenum blue complex from up to 200ml of water into a relatively small

volume of hexanol, so that a considerable increase in sensitivity is obtained.

Interferences

Relatively free from interferences. Arsenates react with the molybdate reagent to produce a

blue colour similar to that formed with phosphate. Concentrations as low as 0.1 mg As/L

interfere with the phosphorus determination. Hexavalent chromium and NO2- interfere to give

results about 3% low at concentrations of 1 mg/L and 10 to 15% low at 10 mg/L. Silfide (NaS)

and silicate do not interfere at concentrations of 1.0 and 10 mg/L.

Minimum detectable concentration is approximately 10 g P/L. P ranges as follows:

Approximately P range mg/L Light path (cm)

0.30-2.0 0.5

0.15-1.30 1.0

0.01-0.25 5.0

Apparatus

1. Colorimetric equipment: one of the following is required:

a. Spectrophotometer, with infrared phototube, for use at 880 nm, providing a

light path of 2.5 cm or longer.

b. Filter photometer equipped with a red colour filter and a light path of 0.5 cm

or longer

2. Acid washed glassware. Glassware should be left in sulphuric acid until required for

use.


49 E25-0177/05

Reagents

1. Sulphuric acid, H2SO4, 5N: dilute 70 mL of conc. H2SO4 to 500 mL with distilled

water

2. Potassium antimonyl tartrate solution: dissolve 1.3715 g K(SbO)C4H4O6. ½ H2o in

400 mL distilled water in a 500 mL volumetric flask and dilute to volume. Store in a

glass-stoppered bottle.

3. Ammonium molybdate solution: dissolve 20 g (NH4)6Mo7O24.4H2O in 500 mL of

distilled water. Store in a glass stoppered bottle.

4. Ascorbic acid, 0.1M: dissolve 1.76 g ascorbic acid in 100 mL distilled water. The

solution is stable for about 1 week at 4⁰C.

5. Combined reagent: mix the above reagents in the following proportions for 100 mL of

the combined reagent: 50 mL 5N H2SO4, 5 mL potassium antimonyl tartrate solution,

15 mL ammonium molybdate solution, and 30 ml ascorbic acid solution. Mix after

each addition of reagent. Let the reagents reach room temperature before they are

mixed and mix in the order provided. If the turbidity forms int he combined reagent,

shake and let stand for a few minutes until turbidity disappears before proceeding. The

reagent is stable for 4 hours.

6. Stock phosphate solution: dissolve in distilled water 219.5 mg anhydrous KH2PO4 and

dilute to 1000 mL; 1.00 mL = 50.0 g PO43- - P.

7. Standard phosphate solution: dilute 50.0 mL stock phosphate solution to 1000 mL

with distilled water; 1.00 mL = 2.50 g P

8. Phenolphthalein indicator solution. Dissolve 0.5g of phenolphthalein in 50ml of 95%

ethyl alcohol, and add 50 ml of distilled water. Add a dilute(e.g. 0.01 or 0.005 mol/l)

CO2 free solution of sodium hydroxide, a drop at a time, until the indicator turns

faintly pink.

Procedure

A. Treatment of sample

1. Pipet 50.0 mL of saple into a clean test tube or 125 mL flask.

2. Add 0.05 mL (1 drop) phenolphthalein indicator. If a red colour develops add 5N,

H2SO4 solution dropwise to just discharge the colour.

3. Add 8.0 mL combined reagent and mix thoroughly

4. After at least 10 min but no more than 30 min, measure absorbance of each sample at

880 nm, using reagent blank as the reference solution.


50 E25-0177/05

B. Correction for turbidity or interfering colour

5. Natural colour of water generally doesn’t interfere at the high wavelength used. For

highly coloured or turbid waters, prepare a blank by adding all the reagents except

ascorbic acid and potassium antimonyl tartrate to the sample. Subtract blank

absorbance of each sample.

C. Preparation of calibration curve

6. Prepare individual calibration curves from the series of 6 standards. Use a distilled

water blank with combined reagent to make the photometric readings for the

calibration curve.

7. Plot absorbance versus phosphate concentration to give a straight line passing through

the origin.

8. Test at least 1 phosphate standard with each sample.

Calculation

mg P/L =

Equation 3.7

3.2 Formulation of relations between variables and parameters The next step is to formulate relations between variables and parameters. For certain classes of

systems these relations have been established already long ago. Consistency is important. This

involves fixing functions to data. For the purpose of this study linear functions and intrinsically

linear functions will be used.

The term ‘linear’ in linear regression means that the function is a linear combination of basis

functions. With the basis function {1,x}, linear functions are those that can be written in the

form:

y= β0 + β1x Equation 3.8

while ‘intrinsically linear functions are those functions that can be written in the form:

y= β0f0 + β1f1 + β2f2 + β3f3 + .... + βnfn Equation 3.9

where βi are constants and the fi are the basis functions.

3.3 Non-dimensionalization This is the partial and full removal of units from an equation involving physical quantities by a

suitable substitution of variables. This technique can simplify and parametrize problems where


51 E25-0177/05

measured units are involved. It is closely related to dimensional analysis. To non-

dimensionalize a system the following steps are taken:

1. Identify all the independent and dependent variables.

2. Replace each of them with a quantity scaled relative to a characteristic unit of measure

to be determined.

3. Divide through by the co-efficient of the highest order polynomial or derivative term.

4. Choose judiciously the definition of the characteristic unit for each variable so that the

coefficients of as many as possible become 1.

5. Rewrite the system of equations in terms of their new dimensionless quantities.

3.4 Solution of model equations Since most models are in the first instance not tractable, it is good policy to solve the model

equations before preliminary test application. The stage involves the search for mathematical

solutions.

3.5 Preliminary test application Depending on the water quality variable, the analytical solution of this equation may be simple

or complex and even impossible for some cases. The distributed source terms (especially

distributed loads with flow) make the analytical solution very complex or even impossible,

when several water quality variables that depend on each other have to be expressed in form of

a system of differential equations.

3.6 Model Verification It is the testing of the calibrated model against the additional set of field data preferably under

different environmental conditions (river flow, waste load etc.), to further examine the range of

validity of the calibrated model. Collection of data for validation is such that, calibration

parameters are fully independent of the validation data. The model so verified can be used for

forecasting of water quality under a variety of perturbed environmental conditions. If a

numerical way has been found to solve the model, it remains to explore the solution as a

function of the independent variables and the adjustable parameters.

3.7 Reiteration of steps 2-6 After comparing the measured data and the calculated solution, one can improve on the model

and this is done by repeating steps 2-6. In view of the iteration process, it is convenient to

follow a modular approach from the beginning.


52 E25-0177/05

3.8 Implementation If the model suffices then it is implemented. This means that the results can be used by non

mathematicians. The appropriate presentation of the results is an important part of the

implementation process.


53 E25-0177/05

4. DATA COLLECTION/ SAMPLING

4.1 Catchment characteristics The characteristics of Ngong' River were observed to change dramatically from its sections

upstream to the sections downstream, through the sprawling Kibera slum and finally at the last

sampling station at the outskirts of Easlands area after its passage through Nairobi’s Industrial

Area.

Site 1: Kariaini Dam:

Below is a picture of sample collection at the 1st sampling station – Kariani Dam.

Diag 4.1: water collection at the 1st sampling point – Kariani Dam.

Observations - the water was brown in colour, odourless and flows slowly from its inlet

through to its outlet. The estimated dimensions of the dam are 225 m long and 30 m wide

at its widest section. The water is used by the people around for cooking, cleaning and

drinking after mild treatment methods such as sedimentation and boiling. The dam is also

used by some surrounding residents for recreational activities such as boat riding and

fishing.

Site 2: Jamhuri Dam outlet:

A picture of the sampling point is shown on the next page.


54 E25-0177/05

Water that flows from this point is clear and odourless.

Diag 4.2: the water that flows out of Jamhuri grounds to then flow into Kibera.

Observations – the area around is heavily polluted with human waste, plastic bags, rotten

food, rags, broken bottles and other rubbish. The water from the exit point out of Jamhuri

Park is however clear as if flows into the river. The water flows at a moderate rate. The

section of the river at this point is about 0.3 m long and 0.2 m at its deepest section.

Beyond this point there are many dumpsites and the water becomes brackish at the point

where the river flows under the bridge seperating Jamhuri and Kibera. The area has no

visible sewerage network and open sewers are seen to plainly discharge their material

straight into the river.

A picture of Ngong' River as it flows below the bridge separating Jamhuri from Kibera is

shown in the next page.


55 E25-0177/05

Diag 4.3: the river as it flows from the bridge separating Jamhuri and Kibera.

Site 3: Kibera bridge:

A picture of the process of sample collection at the Kibera Bridge is shown in the picture

below:

Diag 4.4: the Ngong' River as it passes through Kibera.

Observations – this is visibly one of the most polluted sections of the river. The water is

brackish and polluted with sediments from the surface runoff along the embankments of

the river and the discharge of raw sewage directly into the river. The water has a foul

odour and has a sluggish flow. Open sewers discharge their wastewater directly into the

river. The area is surrounded by dumpsites containing plastic bags, rotten food, rags,

broken bottles and other rubbish.


56 E25-0177/05

Site 4: Weir of Nairobi Dam:

The picture of the Nairobi Dam as it is currently overridden with hyacinth (plate 4.5) is

shown in the following page. The weir is shown in plate 4.6.

Diag 4.5: the Nairobi dam is completely engulfed in Hyacinth

Diag 4.6: the weir at the outlet to Nairobi Dam.

Observations – the Nairobi dam is covered with hyacinth that has colonized the whole

dam from the shores of the dam to the centre. The water flowing over the weir is brackish

and foams heavily after each of the 2 drops. The weir is 3 m wide. Beyond the weir the

river goes through a channelized section 230 m long and a cross-sectional width of 0.6 m

and a depth of 0.2 m. It has an initial drop of 0.5 m and a 2nd drop of approximately 6.5m.

The river passes through Langata Prison and continues under the Mbagathi Way Bridge

into Madaraka area.


57 E25-0177/05

Site 5: Dunga Rd bridge:

A picture of the sample collection process at the 5th sampling station at the Dunga Road

Bridge is shown below:

Diag 4.7: the channelized section of Ngong' River as it flows through the industrial area.

Observations –this sampling point is next to Mater Hospital. This section of Ngong' River

is channelized as it passes through Industrial Area. The channel is 4.0 m wide and 0.6 m

deep. The water is brackish and slow moving. The embankments are littered with garbage

– plastic paper bags, sacks, cloths.

Site 6: Outer-ring Rd Bridge:

The level of pollution in the Ngong' River at the Outer-ring Road bridge is shown in plate

4.8 below.

Diag 4.8: water flowing under the bridge on Outer-ring road.


58 E25-0177/05

Observations –the water appears to be heavily polluted. The water is black in colour and

has a foul smell. The embankments are littered with garbage- plastic bags, rags, broken

bottles and other rubbish. The river is slow moving with some areas being wider than

others due to dumping of waste into the river and sedimentation of soil due to the decrease

in flow rate. At the sampling point the river was 2.5 m wide.


59 E25-0177/05

5. SAMPLE RESULTS AND DATA ANALYSIS

5.1 Longitudinal Profile The altitudes of the various sampling points and the distance between them were obtained from

previous studies carried out by NRBP- Phase II and NEMA reports. The data collected from

this desk study is shown in Table 5.1 below.

Table 5.1: longitudinal profile of the Ngong' River

SAMPLE

STATION NO.

NAME HASL (M) DISTANCE

(KM)

SLOPE OF

BED = i

1 Kariani Dam 1920 0.0 -

2 Jamhuri Dam

outlet

1850 3.3 0.0212

3 Kibera bridge 1790 5.4 0.0286

4 Weir of Nairobi

dam

1690 8.3 0.0345

5 Dunga Rd

Bridge

1660 12.6 0.0070

6 Outer-ring Rd 1610 15.3 0.0185

The longitudinal profile is plotted in the diagram below that shows the height above sea level

at the various sampling stations.

Diagram 5.1: longitudinal profile of the Ngong' River.

1920

1850

1790

16901660

1610

14501500155016001650170017501800185019001950

0 3.3 5.4 8.3 12.6 15.3

HEIGHT ABO

VE SEA LEV

EL

DISTANCE ALONG THE PROFILE (km)

LONGITUDINAL PROFILE

LONGITUDINAL PROFILE


60 E25-0177/05

5.2 Cross-sectional Profile The cross-sectional properties at the various sampling stations is summarised in the table below.

Table 5.2: sampling stations and cross-sectional properties

sample

station

Name W (m) D(m) A(m2) P (m) SHAPE z= (y/x)

1 Kariani

Dam

30.00 2.30 63.71 31.91 sym

trapezoid

1.0

2 Jamhuri

Dam

outlet

0.30 0.20 0.04 0.55 sym

trapezoid

2.0

3 Kibera

bridge

2.00 0.50 0.75 1.71 sym

trapezoid

1.0

4 Weir of

Nairobi

dam

0.60 0.20 0.12 1.00 sym

rectangle

-

5 Dunga Rd 4.00 0.60 2.4 5.20 sym

rectangle

-

6 Outer-

ring Rd

2.50 0.70 1.26 3.08 sym

trapezoid

1.0

Where:

W = width of the river at the sampling station

D= depth of the river at the sampling station

A= cross-sectional area of river at the sampling station

z= slope of the sides of the embankments.

5.3 Biochemical Oxygen Demand The results from the experiments to determine the BOD5 for the water samples taken are given

in table 5.3 in the page that follows. Also given are the average BOD5 values from the different

rounds of sampling.


61 E25-0177/05

Table 5.3: results of BOD tests

SAMPLE

STATION

ROUND 1

(19.09.10)

ROUND 2

(26.09.10)

ROUND 3

(10.10.10)

ROUND 4

(17.10.10)

AVE BOD5

1 73.22 65.41 69.22 63.06 67.72

2 55.17 53.53 59.37 65.37 58.36

3 146.00 147.33 141.91 153.39 147.16

4 134.67 137.25 141.91 131.37 136.30

5 282.35 285.15 293.07 321.57 295.53

6 313.73 320.00 328.00 356.44 329.54

The BOD value at station 1 is 67.72 mg/L. This decreases to 58.36mg/L at station 2. The

decrease could be attributed to self purification of the river at the Jamhuri Dam. The BOD

increases to 147.16 mg/L at station 3 – Kibera Bridge. The increase in BOD could be attributed

to the disposal of raw human waste and other organic rubbish directly into the river. When

these decompose, they use up oxygen from the river resulting in a high BOD value. The BOD

reduces to 136.30 mg/L at station 4 – weir at outlet of Nairobi Dam. This reduction in BOD

could be attributed to the fact that Nairobi Dam acts as a stabilising pond of the Ngong' River

as it flows out of Kibera slum. The BOD increases to 295.53 mg/L at station 5- Dunga Road

Bridge. By the time the river has reached this point, it has passed close to informal settlements,

pharmaceutical, inks, dyes, edible oils and flour companies. The BOD level is highest at station

6 – on Outering Road bridge. It is understandable that this would register the highest BOD

levels because by this point the river has passed through the Eastlands area that is characterised

by numerous abattoirs, car-washes, human settlements and industries. It is also victim to being

used as a garbage disposal site as is evident at the sampling station.

The BOD5 from the different sampling stations is shown in the diagram on the next page in

diagram 5.3a and the average line graph shown in diagram 5.3b.


62 E25-0177/05

Diagram 5.3a: BOD5 from the sampling stations

Diagram5.3b: Average BOD5 across the sampling stations

5.4 Ammonia A key limitation of the Palintest method is the range of ammonia concentration that is can read.

It has a maximum of 1.00mg/L. Ammonia results are as follows in table 4.4 in the page that

follows:

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

0 3.3 5.4 8.3 12.6 15.3

BOD5 (m

g/L)


BOD

19.09.10

26.09.10

10.10.10

17.10.10

67.72 58.36

147.16 136.30

295.53329.54

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

0 3.3 5.4 8.3 12.6 15.3

AVE. BOD 5


AVERAGE BOD5

ave BOD5


63 E25-0177/05

Table 5.3: results from ammonia testing at the sampling stations

SAMPLE

STATION

19.09.10 26.09.10 10.10.10 17.10.10 ave ammonia reading

mg/L N

1 0.06 0.06 0.05 0.11 0.07

2 0.01 0.01 0.02 0.04 0.02

3 1.00 1.00 1.00 1.00 1.00

4 1.00 1.00 1.00 1.00 1.00

5 1.00 1.00 1.00 1.00 1.00

6 1.00 1.00 1.00 1.00 1.00

Ammonia usually originates from specific industrial discharges and human waste such as urine

or from anaerobic decomposition of organic matter. It is highly toxic to fish and other aquatic

animals in concentrations greater than 5 mg/L. The ammonia levels at samples stations 1 and 2

are low indicative of the fact that the river at these 2 points is relatively free of human waste

pollution. However, there is an escalation in level of ammonia from sample stations 3 to 6.

This is indicative of heavy human waste pollution. It was observed especially that in the Kibera

slum there were toilets that were erected over the river or which visibly discharged their waste

material into the river.

The ammonia concentration is shown in the diagrams that follow:

Diagram 5.4a: Ammonia values from the sampling stations

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 3.3 5.4 8.3 12.6 15.3

AMMONIA (m

g/L)


AMMONIA

19.09.10

26.09.10

10.10.10

17.10.10


64 E25-0177/05

The water sample taken from the weir at the outlet of Nairobi also demonstrated high levels of

ammonia. From previous study, it can be shown that this could be due to anoxic conditions

present is the dam. This is because wetlands are a major source of ammonia arising from

anaerobic decomposition of organic matter. The concentrations of free ammonia further on

downstream was further indication of discharge of domestic sewage and industrial waste as

well as existence of anoxic conditions in the river. These would arise to anaerobic breakdown

of organic matter where ammonia is one of the by-products.

Diagram 5.4b: Average ammonia values from the sampling stations

5.5 Nitrite Results from the samples for the presence of nitrite are given in the table that follows.

0.07 0.02

1.00 0.95 1.00 1.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 3.3 5.4 8.3 12.6 15.3

AVE AMMONIA (m

g/L)


ave ammonia

ave ammonia


65 E25-0177/05

Table 5.5: results from the nitrite testing from the sampling stations

SAMPLE

STATION

19.09.10 26.09.10 10.10.10 17.10.10 ave

NITRITE

(mg/L)

1 0.05 0.05 0.05 0.10 0.06

2 0.05 0.05 0.05 0.10 0.06

3 0.45 0.45 0.45 0.40 0.44

4 0.1 0.1 0.1 0.10 0.10

5 0.15 0.15 0.15 0.15 0.15

6 0.2 0.2 0.2 0.30 0.23

The nitrite concentration in the river is relatively low throughout the profile of the river and is

indicative of the relative low presence of nitrites. Most nitrogenous material in natural water

sources tend to be converted into nitrates, so nitrites should be considered as potential sources

of organic nitrates. Nitrites are also consumed by plants and other organisms. Major industries

that contribute to nitrite concentration in water sources include – nitrogenous fertilizer,

miscellaneous industrial inorganics, fertilizer mixing, explosives and canned foods. Nitrite

concentrations can be increased by the biochemical reduction of nitrates by denitrification

processes, usually under anaerobic conditions.

Nitrite concentration in the sampling stations upstream was lower than for sampling stations

downstream. The concentration rose to 0.44 mg/L at sampling station 3 – Kibera Slum. This

was indicative of inflow of human waste into the river. The concentration of nitrite decreased

to 0.1 mg/L at the weir of Nairobi Dam. This reduction in concentration of nitrite is attributed

to consumption by the hyacinth weed that has colonised the dam. The concentration increased

slightly to 0.15 mg/L at the Dunga Rd bridge sampling station and then rose to 0.23 mg/L at

the Outering Rd bridge sampling station. This increase would be attributed to further pollution

from the industries in Industrial area that discharge their effluent into the river as well as the

product of human waste being disposed into the river due to the informal settlements along the

bank.

The nitrite concentration at the various sampling stations is shown in the diagrams that follow.


66 E25-0177/05

Diagram 5.5a: Nitrite results from the sampling stations

The average nitrite concentration gotten from the mean of the sampling done is shown in

diagram 5.5b below.

Diagram 5.4b: average nitrite results from the sampling stations

5.6 Nitrates The determination of nitrates involves the preparation of a stock solution and the plotting of the nitrates to absorbance diagram to facilitate the determination of the concentrations of nitrates from samples collected. The standard graph is found in the following page.

00.050.1

0.150.2

0.250.3

0.350.4

0.450.5

0 3.3 5.4 8.3 12.6 15.3

NITRITE

(mg/L)


NITRITE

19.09.10

26.09.10

10.10.10

17.10.10

0.06 0.06

0.44

0.100.15

0.23

0.000.050.100.150.200.250.300.350.400.450.50

0 3.3 5.4 8.3 12.6 15.3

AVE. NITRITE

(mg/L)


ave NITRITE (mg/L)

ave NITRITE (mg/L)


67 E25-0177/05

Diagram 5.6a: standard solution line

The table that follows gives the results from the test for nitrates in the samples taken from the river.

Table 5.6: results from the tests done on the samples from the sampling stations

sample

station

19.09.10 26.09.10 10.10.10 17.10.10 ave NITRATES (mg

NO3-/L)

1 30.00 32.00 40.00 20.00 30.50

2 60.00 40.00 50.00 32.00 45.50

3 20.00 40.00 60.00 20.00 35.00

4 20.00 40.00 40.00 20.00 30.00

5 100.00 120.00 100.00 120.00 110.00

6 70.00 40.00 20.00 28.00 39.50

Nitrate concentrations in excess of 5 mg/L usually indicate pollution by human or animal

waste, or fertiliser run-off. In cases of extreme pollution, concentrations may reach 200 mg/L.

The WHO has recommended that the maximum nitrate concentration for water for human

consumption should not exceed 50 mg/L as waters with high concentration can exhibit a high

health risk.

y = 0.0393x + 0.0257

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

0 10 20 30 40 50

ABS

NITRATES (ml)

NITRATES

Series2

Linear (Series2)


68 E25-0177/05

The nitrate concentration at sampling station 1 – Kariani dam was 30.50 mg/L and this

increased to 45.5 mg/L at sampling station 2 – Jamhuri Dam outlet. The concentration then

reduced to 35.00 mg/L at the Kibera bridge sampling station. This reduction in nitrate

concentration could be attributed to anaerobic conditions present in the river at this point

preventing the oxidation of nitrogenous material. The nitrate concentration decreases further to

30.0 mg/L at the weir of Nairobi dam. This may be due to uptake by the hyacinth that has

colonised the dam. The nitrate concentration at sampling station 5 is the highest recorded from

all the sampling stations at an average nitrate concentration of 110 mg/L. This reduces to 39.5

mg/L at the last sampling station.

The results have been plotted and appear on the graph below.

Diagram 5.6b: Nitrate results from the sampling stations

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

0 3.3 5.4 8.3 12.6 15.3

NITRA

TES (m

g/L)


NITRATE

19.09.10

26.09.10

10.10.10

17.10.10


69 E25-0177/05

Diagram 5.6c: average Nitrate results from the sampling stations

5.7 Phosphates The determination of phosphates involves the preparation of a stock solution and the plotting of the phosphate to absorbance diagram to facilitate the determination of the concentrations of phosphates from samples collected. The standard graph is found in the following page.

Diagram 5.7a: the standard concentration phosphates diagram.

30.50

45.5035.00 30.00

110.00

39.50

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0 3.3 5.4 8.3 12.6 15.3

ave NITRA

TES (m

g/L)


ave NITRATES (mg NO3‐/L)

ave NITRATES (mg NO3‐/L)

y = 0.0294x ‐ 0.0372

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0 20 40 60

ABS

PHOSPHATES (ml)

PHOSPHATES

Series1

Linear (Series1)


70 E25-0177/05

The results from the tests done to determine the phosphates concentrations are shown in the table that follows:

Table 5.7: phosphate results from the tests done on the samples from the sampling stations

sample

station

19.09.10 26.09.10 10.10.10 17.10.10 ave PHOSPHATES

(mg/L)

1 0.00 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00 0.00

3 0.91 1.10 0.96 1.03 1.00

4 0.65 0.72 0.55 0.50 0.60

5 0.62 0.77 0.55 0.58 0.63

6 1.01 1.03 0.99 0.84 0.97

There can be considerable fluctuations in phosphate concentrations in surface waters.

Phosphates are rarely found in high concentrations in fresh waters as it is actively taken up by

plants (Chapman D. et al, 1997). The concentrations of phosphates in sampling stations

upstream, registered absence of phosphates with an average of 0.00 mg/L from samples taken

at the different sampling times. The highest concentration of phosphates in the Ngong' River

was found at sampling station 3 – Kibera slum, where the concentration was 1.00 mg/L. This is

indicative of presence of pollution from domestic wastewaters, especially those containing

detergents and industrial effluents. The concentration decreases to 0.60 mg/L at the weir of

Nairobi dam. This reduction could be attributed to active uptake of phosphates by the hyacinth

present on the surface water of the Nairobi Dam. The phosphate concentration at sampling

station 5 is relatively the same as the for the previous station with a difference of 0.03 mg/L.

The concentration goes up to 0.97 mg/L at the Outer-ring Rd bridge (sampling station 6),

which can be attributed to pollution from the industries in the Industrial area of Nairobi and

numerous car washes and motor vehicle garages that discharge their effluent into the Ngong'

River.

The results are plotted and shown in the following diagrams; 4.7a and 4.7b.


71 E25-0177/05

Diagram 5.7b: Phosphate results from the sampling stations

Diagram 5.7c: Average Phosphate results from the sampling stations

5.8 Comparison of results from 2003 with 2010 From the initial base data obtained by the Nairobi River Basin Project –Phase II carried out in

2003, the following data is available for the preparation of the model awaiting further

calibration using present day results. These results are shown in the table on the next page:

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 3.3 5.4 8.3 12.6 15.3

PHOSPHATES (m

g/L)


PHOSPHATES

19.09.10

26.09.10

10.10.10

17.10.10

0.00 0.00

1.00

0.60 0.63

0.97

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 3.3 5.4 8.3 12.6 15.3

ave PH

OSPHATES (m

g/L)


ave PHOSPHATES (mg/L)

ave PHOSPHATES (mg/L)


72 E25-0177/05

Table 5.8: results from the NRBP- Phase II, for BOD and nutrients.

Base station BOD5 (mg/l) NH4-N

(mg/l)

NO3-N

(mg/l)

NO2-N

(mg/l)

PO4-P

(mg/l)

Kariani Dam

(2)

73.00 1.40 - 0.00 0.10

Jamhuri

Dam outlet

(3)

35.00 1.60 - 0.02 0.14

Kibera

bridge (5)

147.50 35.00 - 0.00 0.33

Weir of

Nairobi Dam

(8)

64.00 33.00 - 0.01 0.32

Enterprise

Rd bridge

(13)

370.00 39.00 - 0.07 2.91

Outerring

Rd bridge

(14)

403.00 33.00 - 0.01 2.03

There are no comparable results for nitrate tests. These results from 2003 are compared with

the results from testing in 2010 and are shown in the diagrams 5.8 to 5.12.


73 E25-0177/05

Diagram 5.8: comparison of BOD in 2003 versus 2010

It can be concluded that the BOD levels are generally the same when compared between 2003

and 2010. This implies that pollution levels have remained constant over time.

Diagram 5.9: comparison of nitrite in 2003 versus 2010

The nitrite levels are conspicuously lower in 2003 than in 2010. The difference in nitrite levels

could be due to a number of reasons:

• Different testing methods between NRBP Phase II and the current method

• An increase in nitrite pollution in 2010 when compared to 2003.

0

100

200

300

400

500

0 2 4 6 8

BOD (m

g/L)

SAMPLE STATIONS

COMPARISON OF BOD IN 2003 VS 2010

BOD (2003)

BOD (2010)

0

0.1

0.2

0.3

0.4

0.5

0 2 4 6 8

NITRITE

(mg/L)

SAMPLE STATIONS

COMPARISON OF NITRITE IN 2003 VS 2010

NITRITE 2003

NITRITE 2010


74 E25-0177/05

It is also noted that sample station 3 had the highest nitrite concentration in 2010, up from

0.0 mg/L in 2003 to 0.44 mg/L.

Diagram 5.10: comparison of phosphate in 2003 and 2010

The phosphate concentration at some of the sampling points is lower in 2010 in comparison to

2003. The phosphate concentration at sample station 5 in 2003 is higher than at any other

station. This can be attributed to high phosphate pollution due to industrial discharge.

00.51

1.52

2.53

3.5

0 2 4 6 8

PHOSPHATE

(mg/L)

SAMPLE STATIONS

COMPARISON OF PHOSPHATE IN 2003 AND 2010

PHOSPHATE 2003

PHOSPHATE 2010


75 E25-0177/05

6. MODELLING For this particular project the variables under consideration are BOD (=CBOD/1.5), ammonia

nitrogen, nitrate nitrogen, nitrite nitrogen and phosphate phosphorus.

SISMOD 0.9.5 is a one dimensional steady state model, which assumes that the stream cross-

section is known. The software is designed to be compatible with multi reach streams, where

locally uniform flow conditions can be assumed. SISMOD contains algorithms, which enable

following features:

• The model can determine which stream reaches should be calculated prior to

other reaches according to upstream - downstream topology.

• Point and diffuse source based pollutant loads can be incorporated to mass

balance.

• Hydraulic calculations can be conducted for symmetric triangular, symmetric

trapezoidal and rectangular cross sections.

• Start and end location of aerobic and anaerobic conditions can be detected and

valid equations are selected for these sections.

6.1 THE WATER QUALITY MODEL

6.1.1 Model Network The model assumes that the stream is composed of reaches. Along a reach,

• Stream bed geometry (cross-sectional area, bed slope etc.)

• Water temperature, salinity, average level of water surface with respect to sea level

• Kinetic characteristics in the water quality model,

are assumed to be constant.

For the purpose of this study it is determined that the section of the river is a standard reach.

6.1.2 Model Inputs:

STREAM SYSTEM

The stream system consists of 1 main stream with ZERO tributaries

There are ZERO point discharges


76 E25-0177/05

The hydraulic and geometric properties of the stream are shown in the table below:

Table 6.1: hydraulic and geometric properties of the stream

sample station

Name SHAPE CHANNEL DESCRIPTION

Manning’s n

1 Kariani Dam

sym trapezoid

clean, winding, some pools and shoals

0.040

2 Jamhuri Dam outlet

sym trapezoid

same as above, lower stages, more ineffective, slopes and sections

0.048

3 Kibera bridge

sym trapezoid

sluggish reaches, weedy,

0.070

4 Weir of Nairobi dam

sym rectangle Float finish concrete

0.015

5 Dunga Rd sym rectangle Float finish concrete

0.015

6 Outer-ring Rd

sym trapezoid

sluggish reaches, weedy,

0.070

Model Inputs Related to Reaches The entire profile of the river under consideration is considered to be on the standard reach. Details are contained in the table located on the next page:


77 E25-0177/05

Table 6.2: reach properties for the stream

Stream Reach

Reach No reach type x-section No

Discharge No

D/stream reach

No upstream reaches

mainstream reach 0-3.3 km

1 2 3 No discharge

2 1

mainstream reach 3.3-5.4 km

1 2 3 No discharge

2 1


1 2 2 No discharge

2 1


1 2 2 No discharge

2 1


1 2 3 No discharge

2 1

KEY: Stream reach type 1: Headwater reach Stream reach type 2: Standard reach Stream reach type 3: End reach

Channel geometry = 1: Symmetric triangle cross section Channel geometry = 2: Rectangle cross section Channel geometry = 3: Symmetric trapeze cross section Channel geometry = 4: Irregular cross section

Model Inputs Related to Stream Cross Sections

Channel bottom slope is dependent on the section of the river. The stream cross-sectional properties are given inbelow:


78 E25-0177/05

Table 6.3: stream cross-section properties

valid distance

x-section No manning's n x-section type x-section data

B i

mainstream reach 0-3.3 km

1 0.048 3 0.30 0


1 0.070 3 1.30 0.021212121


1 0.015 2 0.60 0.028571429


1 0.015 2 4.00 0.034482759


1 0.070 3 2.50 0.006976744

Model Inputs Related to Diffuse Loads without Flow

(the diffuse loads are not known in this experiment)

Model Inputs Related to Diffuse Loads with Flow

(the diffuse loads are not known in this experiment)

6.1.3 Hydraulic Calculations: In order to calculate the changes in the concentration of a water quality parameter along the

stream, flows and depths along the stream should be known. As measurement of these

parameters along the stream is not possible, they should be calculated. As the dispersion is

neglected, and the model is a steady state model, flows and depths will be calculated by

Manning Formula.

v = R2/3i1/2 Equation 6.1


79 E25-0177/05

Where:

v = velocity

n= Manning roughness coefficient

R= Hydraulic radius = wet area/wet perimeter (m)

i= Channel bed slope

Hydraulic calculations shall be carried out for 2 principle types of cross-sections; namely

symmetrical trapezoidal and symmetrical rectangular cross-sections. The details of channel

properties are given in the table below:

Table 6.4: channel properties at sampling stations

sample station Name SHAPE CHANNEL

DESCRIPTION

Manning’s n

1 Kariani Dam sym trapeze clean, winding,

some pools and

shoals

0.040

2 Jamhuri Dam

outlet

sym trapeze same as above,

lower stages,

more ineffective

slopes and

sections

0.048

3 Kibera bridge sym trapeze sluggish reaches,

weedy,

0.070

4 Weir of Nairobi

dam

sym rectangle Float finish

concrete

0.015

5 Dunga Rd sym rectangle Float finish

concrete

0.015

6 Outer-ring Rd sym trapeze sluggish reaches,

weedy,

0.070

(Chow, 1959).

6.1.4 Water quality calculations Each of the pollutant types has its own unique equation explaining the distribution of pollutants

along its profile.

6.1.4.1 BOD Under aerobic conditions, BOD is calculated using the following formula:


80 E25-0177/05

L = L0 exp (-Kr ) Equation 6.2

Where,

x : Distance along the stream reach (km)

U : Current velocity along the stream reaches (m·s-1)

L0 : BOD concentration at the beginning of stream reaches (mg·L-1)

Kr : Total BOD utilization rate (oxidation and settling) (day-1)

L : BOD concentration (mg·L-1)

The process of non-dimensionalization (the partial and full removal of units from an equation

involving physical quantities by a suitable substitution of variables) results in the following

equation:

L = L0 exp

. Equation 6.2b

6.1.4.2 Ammonia nitrogen Under aerobic conditions, ammonia nitrogen is calculated using Equation 6.3

N2 = N2,0 exp(-nitr2,2 ) Equation 6.3

Where,



nitr2,2 : Total ammonium nitrogen utilization rate (utilization by plants and phytoplankton,

nitrification) (day-1)

N2,0 : Ammonium nitrogen concentration at the beginning of stream reach (mg·L-1)

N2 : Ammonium nitrogen concentration (mg·L-1)



equation:

N2 = N2,0 exp,

.) Equation 6.3b


81 E25-0177/05

6.1.4.3 Nitrate nitrogen Under aerobic conditions, nitrate nitrogen is calculated by Equation 2.4

N3 = ,, ,

[ exp (-nitr2,2 ) –exp(-nitr3,3 ) ] N2,0 + [ exp(-nitr3,3 ) ] N3,0

Equation 6.4

Where,



nitr2,3 : Nitrification rate (day-1)

nitr3,3 : Total nitrate nitrogen utilization rate (utilization by plants and phytoplankton,

denitrification) (day-1)


N3,0 : Nitrate nitrogen concentration at the beginning of stream reach (mg·L-1)

N3 : Nitrate nitrogen concentration (mg·L-1)



equation:

N3 = ,, ,

* .

[ exp (-nitr2,2 ) – exp(-nitr3,3 ) ] N2,0

+ .

[exp (-nitr3,3 ) ] N3,0 Equation 6.4b

6.1.4.4 Nitrite Nitrogen The general formula for the calculation of nitrogen oxide compound concentrations is given by

Equation 2.5

N4 = ,, ,

[ exp (-nitr2,2 ) –exp(-nitr4,3 ) ] N2,0

+ [ exp (-nitr4,3 ) ] N4,0 Equation 6.5


82 E25-0177/05

Where:

nitr2,3: Nitrification rate constant (day -1)



nitr4,3: Total nitrite nitrogen utilization rate constant.


N4,0 : Nitrite nitrogen concentration at the beginning of stream reach (mg·L-1)



equation:

N4 = ,, ,

*.

[ exp (-nitr2,2 ) –exp(-nitr4,3 ) ] N2,0

+ .

[ exp (-nitr4,3 ) ] N4,0 Equation 6.5b

6.1.4.5 Phosphate phosphorus Under aerobic conditions, phosphate phosphorus concentration is calculated by Equation 2.6

P2 = P2,0 exp (- phos2,2 ) Equation 6 .6

where,



phos2,2 : Total phosphate phosphorus utilization rate (utilization by plants and

phytoplankton etc.) (day-1)

P1,0 : Organic phosphorus concentration at the beginning of stream reach (mg·L-1)

P2,0 : Phosphate phosphorus concentration at the begin of stream reach (mg·L-1)

P2 : Phosphate phosphorus (mg·L-1)


83 E25-0177/05



equation:

P2 = P2,0 exp ,

.) Equation 6.6b

6.2 Solution to model equations and Preliminary test application: Using regression analysis, the following solutions were derived for the equations representing

the BOD and nutrient loading in the river:

6.2.1 BOD The solved model equation gives the equation:

L = L0 exp(0.000116x + 0.013587U -0.1545) Equation 6.7

The co-efficient of determination for this formula (r2) is 0.885453

Thus Kr = - 8.538*10-3 (day -1)

Results for the regression analysis are shown in appendix 9.6.1

6.2.2 Ammonia The solved model equation is:

N2 = N2,0 exp (0.00016x + 0.411692U -0.23777) Equation 6.8


Thus nitr2,2= - 3.886*10-4 (day -1)


6.2.3 Nitrate The solved model equation is:

N3 = -52.48 [exp(0.00016x + 0.411692U -0.23777)-(exp(0.004063 )] N2,0

+[0.004063 ] N3,0

= N3,0 exp(0.0000193x + 0.143822U – 0.01228) Equation 6.9

Thus nitr2,2 : - 3.886*10-4 (day -1)

nitr2,3 : 0.19284 (day -1)


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nitr3,3 : - 0.004063 (day -1)



6.2.4 Nitrite The solved model equation is:

N4 = -13.25 [exp(0.00016x + 0.411692U -0.23777)-(exp(-0.00015524 )] N2,0

+(-0.00015524 ) N4,0

= N4,0 exp(0.000085x – 0.13799U + 0.328836) Equation 6.10


Thus nitr2,2 : - 3.886*10-4 (day -1)

nitr2,3 : 0.19284 (day -1)

nitr4,3 : -0.00015524 (day -1)


6.2.5 Phosphates The solved model equation is:

P = P0 exp (0.000131x + 0.092408U – 0.25538) Equation 6.11


Thus phosp2,2 = 1.4176 * 10 -3 (day -1)


6.3 Model Verification and implementation: The model is tested using the results from the last sampling exercise done on the 17th of

October 2010. The flow properties (U) were assumed to be the same as the average used in the

calibration of the model.

The results from the tests are given in the table below. The values of the concentrations at the

beginning of the reach are tested and used to determine the concentrations at the other sections

of the profile.


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Table 6.5: testing results from the model equations

Sample

stations

BOD (mg/L) AMMONIA

(mg/L)

NITRATE

(mg/L)

NITRITE

(mg/L)

PHOSPHATE

(mg/L)

Predicted values from the model

1 63.06 0.11 20 0.018 0.1

2 79.79521401 0.13920187 22.71276511 0.030786 0.126547151

3 102.3393061 0.17852979 25.0010438 0.034897 0.162299805

4 147.3708629 0.25708684 35.66406387 0.033509 0.233715306

5 243.7608804 0.42523815 40.62085617 0.046162 0.38658014

6 323.3811787 0.56413488 30.97167443 0.079196 0.512849893

Actual values from the tests

1 63.05571734 0.11 20 0.018 0

2 65.37254902 0.04 32 0.016 0

3 153.3861386 1.00 20 0.079 1.028571429

4 131.372549 1.00 20 0.021 0.497142857

5 321.5686275 1.00 120 0.027 0.582857143

6 356.4356436 1.00 28 0.064 0.84

The differences between the model results and the actual values from laboratory testing are

shown in the diagrams 6.1 to 6.5 that follow. The residuals between the predicted and actual

values are given in the appendices (appendix 9.6).


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Diagram 6.1: predicted versus actual BOD results for 17.10.10

The independent variables are seen to explain the variation in the independent variable to a

high degree. This is seen by the minimal residue or error between the predicted BOD and the

actual BOD attained by laboratory testing.

Results from the testing for Ammonia are shown in the diagram that follows:

Diagram 6.2: predicted versus actual ammonia results for 17.10.10

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

0 2 4 6 8

bod

sample stations

PREDICTED VS ACTUAL BOD

PREDICTED BOD

BOD ACTUAL

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8

AMMONIA m

g/L

SAMPLE STATIONS

PREDICTED VS ACTUAL AMMONIA

PREDICTED AMMONIA

ACTUAL AMMONIA


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In the case of ammonia, the results from the model equations show a wide difference with the

actual ammonia results obtained from the laboratory testing. The validation of the model can

be determined further by the collection of more samples under different environmental

conditions for use in model calibration.

The results for the testing of nitrates are shown in the diagram that follows:

Diagram 6.3: predicted versus actual nitrate results for 17.10.10

For all stations except station 5, the results from the model equations are seen to be

approximately equal to the values gotten through laboratory testing. The nitrate value for

sample station 5 is taken, in analysis, as an ‘outlier’-a value that can be a source of error and

affects the degree to which the model explains the variance in the dependent variable.

The results from the expected and actual Nitrite concentrations are shown in the diagram

below:

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0 2 4 6 8

NITRA

TE m

g/L

SAPLE STATIONS

PREDICTED VS ACTUAL NITRATE

PREDICTED NITRATE

ACTUAL NITRATE


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Diagram 6.4: predicted versus actual nitrite results for 17.10.10

The predicted concentrations are consistently higher than the actual concentrations for all

stations except station 3, which shows a nitrite concentration of 1.00 mg/L that could be the

result of high levels of pollution into the river.

The results from the testing of the equation for the prediction of phosphate concentration when

compared to actual results from laboratory testing yielded the following diagram:

Diagram 6.5: predicted versus actual phosphates results for 17.10.10

0.00.00.00.00.00.10.10.10.10.1

0 2 4 6 8

NITRITE

mg/L

SAMPLE STATIONS

PREDICTED VS ACTUAL NITRITE

PREDICTED NITRITE

ACTUAL NITRITE

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8

PHOSPHATE

mg/L

SAMPLE STATIONS

PREDICTED VS ACTUAL PHOSPHATES

PREDICTED PHOSPHATES

ACTUAL PHOSPHATES


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Phosphate concentration from the prediction in the model equation increased exponentially

along the profile of the river. They were however lower than the actual results.

6.4 Accounting for pollution input into the river: A key component of any model is its ability to predict the input of pollutants into the river at

various sections along the profile.

In this model the quantity of pollutant that will be taken as discharged into the river at any

section is the difference between the predicted value at the particular chainage and at the flow

velocity specified minus the predicted value at the last pre-determined sample station.

6.5 Capabilities and Limitations of the Water Quality Model The model is a steady state water quality model that can estimate biochemical oxygen demand,

ammonium nitrogen, nitrate nitrogen and phosphate phosphorus concentrations along streams

for steady state. Because it is relatively easy to use and understand, it can be considered as a

useful tool that fills in the gap between manual calculations and complex models.

Its limitations are listed below:

• Hydraulic calculations can only be conducted for uniform flow. Therefore, the depths

and velocities calculated at the river sections, where flow velocities are low or rapidly

changing flow conditions occur will contain errors.

• The model simulates the transport only using advection and neglects dispersion.

Therefore, errors will increase for the aquatic environments (such as transitional

waters reaching the sea), where dispersion is important.

• No correction is made in parameters for deviations in temperature of samples from the

standard 20⁰C.

• Nitrogen and phosphorus cycles do not affect each other like in real aquatic

ecosystems through primary production. Therefore, errors will increase for the aquatic

environments, where primary production is important.

• Linear regression as used to determine the parameters and utilisation constants does

not adequately explain the variance in the data for all nutrient pollutants. The co-

efficient of determination (r2) in some cases is less than 40%.

• We need to collect a lot more data to verify the predictions of the model equations.

This is a key constraint.


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6.6 Discussion: Water quality analyses based on the monitored data do not provide pollutant load distribution

along the entire river network. Such type of spatial information can only be generated if stream

water quality models are used to interpolate the available data. Therefore, the water quality

model developed in this study will further be controlled with the water quality assessment

realized for the selected data.

Water quality models have the advantage of including the essential mechanisms that take place

in the stream directly to the interpolation process, unlike other interpolation and statistical

methods that use/relate data from different locations numerically and partly consider the nature

of the stream. Models also provide other advantages and benefits for further stages of water

pollution control. By using models it is possible to generate scenarios, establish management

plans, project the probable environmental impacts, and estimate the costs of the measures to be

taken. Thus, water quality models are being used as decision-support system tools since they

can comment “better” on the possible future conditions of the aquatic ecosystems and have a

scientific basis for better management plans.

6.7 Conclusions: This study mainly consists of two parts. In the first one, water quality assessment is realized for

the Ngong' River by carrying out laboratory testing on the samples obtained from the pre-

selected sampling stations. The number of monitoring stations needs to be increased in order to

make a better water quality assessment.

Due to the data constraints highlighted in the limitations of the study, the modelling study has

not reached its final stage. The model itself is more suitable for screening through many

different water quality management options, and for determining the ones that could be further

investigated rather than predicting the benefits of individual management options. As more

data become available, more advanced watershed/water quality models with further

components could be developed.

The results derived from the model were found, in some instances, to be consistent with water

quality assessment for some of the pollutant types and not so in others. Such an inconsistency

between the findings of the measured and modelled values indicates that the developed model

needs further refinement by considering other variables that could directly affect and thus

predict the concentrations of these pollutant concentrations. This is essential before the model

can be used for predicting future water quality in case of any changes in the organic matter

and/or nutrient loads.


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7. RECOMMENDATIONS AND WAY FORWARD The pollutant levels within Ngong' River needs to be considered and adequate measures need

to be taken to improve monitoring and deal with inflow of pollutants into the river. The

examples given are not full but instead, present a sample of practical and tangible actions that

can be implemented, in an effort to reduce pollution.

a. Continuous monitoring of the river pollution using the selected base stations.

b. Further study to understand the behaviour of the pollutants and their relationship with

a variety of variables.

c. Industrial discharges should be stopped through efforts by the industries to take

measures to address pollution produced as waste from their production processes

d. Address the discharge of human waste into the river especially at the sections where is

passes through the slum areas- Kibera, Mukuru. This can be initiated by relocation of

human settlement or by diversion of river water, either around such sections or within

enclosed conduits underground.

e. Clean up action plan should be drawn to involve the communities that rely on the

water from Ngong' river.

f. Some technical and financial support should be considered in developing technologies

to treat discharges from the industries within their individual premises.

g. Community sensitisation and awareness programmes to encourage a healthy attitude

towards water resource conservation and use.

h. Polluter- pays principle should be considered and it’s viability as a means of reducing

pollution should be investigated. It can be initiated in order to determine its effect on

lowering the pollution levels of the river.

i. Engineering solutions such as channelling the river or introduction of flow weirs,

especially in areas where stagnant pools exist.

The way forward is:

a. Develop techniques to consistently monitor the levels of pollution within the river and

other freshwater sources.

b. Capacity development is crucial in local authorities to enable them to discharge their

duties to monitor and prevent pollution of riverine water sources.

c. Develop an action plan to coordinate the cleaning effort of Ngong' river and it’s

‘sister’rivers.


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8. REFERENCES: Ali Erturk, Alpaslan Ekdal, Melike Gurel, Kiziltan Yuceil, Aysegul Tanik, 2004. “Use of

mathematical models to estimate the effect of nutrient loadings on small streams.”

Ali Erturk, Melike Gurel, Mansoor Ahmed Baloch, Teoman Dikerler, Evren Varol,

Neslihan Akbulut, and Aysegul Tanik, 2006. “Application of Watershed Modeling

System (WMS) for Integrated Management of a Watershed in Turkey.”

Ali Erturk, 2009. “Simple Stream Model - Version 0.9.5 User Guide and Reference

Manual”

Bartram J. and Balance R., 1995. “Water Quality Monitoring – a practical guide to the

design and implementation of freshwater quality studies and monitoring programmes.”

Bende-Michl, U.2009. “Complementary water quality modelling to support natural

resource management decision making in Australia”

Chapman D. and Kimstach V., 1997. “Water Quality Assessments- A Guide To The Use

Of Biota, Sediments And Water In Environmental Monitoring”,

Gevearts, E. A. L. 1971. Hydrology of the Nairobi Are, Technical report. Water

Department. Howard G. 2002. Private communication (IUCN)

Gonzales, A. E, 2001. Statistical Techniques Applied to Optimization of Sampling

Campaigns in a Reservoir. Proceedings of the third International Workshop on Regional

Approaches to Reservoir Development and Management in the La Plata River Basin:

Informed decision Processes for Sustainable Development of Reservoirs. UNEP-IETC

pp. 262.

Hem J. D., “Study and interpretation of the Chemical Characteristics of Natural Waters”,

1989. WHO Guidelines for Drinking Water Quality. Volume 2. Health Criteria and other

supporting information.

Himesh S., Rao C.V.C. and Mahajan A.U, 2000. “Calibration and Validation of Water

Quality Model, Case 1. River.”

Issaias, I. 2000. Environmental Impact of Urbanization on Water Resources-A case study


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on Nairobi Dam. Imperial College of Science, Technology and Medicine

(University of London).100pp.

Kithia, S. M 2001. The effects of Land Use Changes on the Hydrology and Water Quality

of Upper-Athi River Basin, Kenya. Programme and Abstracts for VLIR-IUCUoN Annual

Postgraduate Seminars 2001.

Krhoda, G. 2002. Nairobi River Basin Project Phase II: The Monitoring and Sampling

Strategy for Ngong/Motoine River. (In press, pp. 55).

Mooney D., Swift R., 1999. “A course in mathematical Modelling.”

Ndede, H. 2002. Baseline Survey and Environmental Impact Assessment for the Nairobi

River Basin Project-Phase II. (In press, pp. 50).

Ngecu & Gaciri 1998. Urbanisation Impact on the Water Resources with major third

world cities: A case study of Nairobi and its Environs. Episodes, Vol. 21 no. 4. PP226.

Ohayo-Mitoko, G. 1996. Concentrations of Heavy Metals, Organochlorine Pesticides,

Organic and Microbial Pollution in the Nairobi River and it’s Tributaries. Dissemination

Workshop on “Concentrations of Heavy Metals, Organochlorine pesticides, Organic and

Microbial Pollution in the Nairobi River and it’s Tributaries”. Sustainable Development

Consultants. 113pp.

Olago & Aketch 2000. Pollution Assessment in Nairobi River Basin. Pollution

Assessment Report of The Nairobi River Basin. Africa Water Network. Okoth, PF &

Otieno P. (eds).106pp. UNEP, 1999. The Nairobi River Basin Project-Project Summary.

UNEP, pp16.

Peter Reichert, Dietrich Borchardt, Mogens Henze, Wolfgang Rauch, Peter Shanahan,

László Somlyódy, Peter A. Vanrolleghem, 2001. “River Water Quality Model No. 1,

Edited by IWA Task Group on River Water Quality Modelling”

Sharon N. Kahara, 2002. “Characterizing Anthropogenic Sources of Pollution for

Tropical Urban River Management: A Proposed Case Study of the Nairobi River Basin. “

Wandiga, S. O 1996. River Pollution In Developing Countries-A case study III: Effect of

Industrial Discharges on Quality of Ngong River Waters in Kenya. Dissemination


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Workshop on “Concentrations of Heavy Metals, Organochlorine pesticides, Organic and

Microbial Pollution in the Nairobi River and its Tributaries”. Sustainable Development

Consultants. 113pp.


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9. APPENDICES:

9.1 Budget: The budget for undertaking the project is summarised in the table below:

ACTIVITY SHILLINGS

BACKGROUND DATA FROM NRBP/NEMA/WARMA

400

SAMPLE COLLECTION/ LABOUR/ MATERIALS

1,500

PRINTING WORK 600

TRANSPORT 2,500

CHEMICAL ANALYSIS (LABORATORY WORK)

5,000

TOTAL 10,000


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9.2 Working schedule:

The working schedule for the project is shown here:


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9.3 The tabulated results from NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report:

The results from the initial study carried out in the NRDP- Phase II are shown in Table 9.1 and Table 9.2 below:

They were much higher in the dry weather than in the wet weather (NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report).

TABLE 9.1

TABLE 9.2


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9.4 The plotted results from NRDP- Phase II, UON/UNEP project, Feb- Nov 2003, Final report:

Diagram 9.1 shows the ammonia concentration along the river profile, diagram 9.2 shows the

nitrite concentration and diagram 9.3 shows the phosphate concentration along the profile.

Diagram 9.1: ammonia concentration along the river (source: fig 3.4 (c) NRDP- Phase II,

UON/UNEP project, Feb- Nov 2003, Final report.)

Diagram 9.2: nitrite concentration along the river (source: fig 3.4 (b) NRDP- Phase II,

UON/UNEP project, Feb- Nov 2003, Final report.)


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Diagram 9.3: phosphate concentration along the Ngong River (source fig 3.4 (a) NRDP-

Phase II, UON/UNEP project, Feb- Nov 2003, Final report.)

9.5 Manning’s co-efficient (Chow, 1959): Mannning’s co-efficient for main channel for use in the manning’s formula was taken from the table below:

Table 9.3: table showing Manning’s n (Chow, 1959).

Type of Channel and Description Minimum Normal Maximum

Natural streams - minor streams (top width at floodstage < 100 ft)

1. Main Channels

a. clean, straight, full stage, no rifts or deep pools 0.025 0.030 0.033

b. same as above, but more stones and weeds 0.030 0.035 0.040

c. clean, winding, some pools and shoals 0.033 0.040 0.045

d. same as above, but some weeds and stones 0.035 0.045 0.050

e. same as above, lower stages, more ineffective slopes and sections

0.040 0.048 0.055

f. same as "d" with more stones 0.045 0.050 0.060

g. sluggish reaches, weedy, deep pools 0.050 0.070 0.080

h. very weedy reaches, deep pools, or floodways with heavy stand of timber and underbrush

0.075 0.100 0.150


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9.6 Summary output from regression analysis for modelling: Regression analysis was used to find the solutions to the model equations. The natural log of the dependent variable was calculated to determine the exponential trendline that best explains the data. The summary output from the determination of the regression line that best fits to explain the pollution distribution along the profile of the river is given below:

9.6.1 BOD For the model, the independent variables were taken to be ‘x’ and ‘U’. The dependent variable was ‘ln’ which is the natural log of (L/L0). The table is shown below.

Table 9.4: table for determination of trendline fitting the data.

sample station

x U x/U L L/Lo ln

1 0 0 0 67.72471365 1 0

2 3300 0.525854867 6275.49578 58.36206562 0.861754336 -0.14878504

3 5400 0.910803375 5928.831788 147.1567657 2.172866562 0.776047292

4 8300 2.990619611 2775.344604 136.3020773 2.012589938 0.699422419

5 12600 3.31706844 3798.53483 295.5348476 4.363766662 1.473335598

6 15300 1.068154092 14323.77606 329.5402834 4.865879318 1.582247443

Summary output:

Regression Statistics Multiple R 0.940985 R Square 0.885453 Adjusted R Square 0.809089 Standard Error 0.314295 Observations 6

ANOVA df SS MS F

Regression 2 2.290748 1.145374 11.59507 Residual 3 0.296343 0.098781 Total 5 2.587091

Coefficients Standard

Error t Stat P-value Intercept -0.1545 0.22919 -0.67411 0.548537 x 0.000116 3.06E-05 3.772518 0.03261 U 0.013587 0.129702 0.104752 0.923183


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RESIDUAL OUTPUT

Observation Predicted

ln Residuals 1 -0.1545 0.154499 2 0.234059 -0.38284 3 0.482006 0.294041 4 0.845445 -0.14602 5 1.346873 0.126463 6 1.628383 -0.04614

9.6.2 Ammonia For the model, the independent variables were taken to be ‘x’ and ‘U’. The dependent variable was ‘ln’ which is the natural log of (N2/N2,0). The table is shown below.


sample station

x U x/U AMMONIA N2/N2,0 ln

1 0 0 0 0.07 1 0

2 3300 0.525854867 6275.49578 0.02 0.285714286 -1.25276297

3 5400 0.910803375 5928.831788 1.00 14.28571429 2.659260037

4 8300 2.990619611 2775.344604 1.00 14.28571429 2.659260037

5 12600 3.31706844 3798.53483 1.00 14.28571429 2.659260037

6 15300 1.068154092 14323.77606 1.00 14.28571429 2.659260037

Summary output:


ANOVA df SS MS F



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Error t Stat P-value Intercept -0.23777 1.052581 -0.2259 0.835797 x 0.00016 0.000141 1.136953 0.338154 U 0.411692 0.595671 0.69114 0.539157

RESIDUAL OUTPUT


ln Residuals 1 -0.23777 0.237774 2 0.506634 -1.7594 3 1.001063 1.658198 4 2.321234 0.338026 5 3.143523 -0.48426 6 2.649597 0.009663

9.6.3 Nitrates For the model, the independent variables were taken to be ‘x’ and ‘U’. The dependent variable was ‘ln’ which is the natural log of (N3/N3,0). The table is shown below.


sample station

x U x/U AMMONIA NITRATE N3/N3,0 ln

1 0 0 0 0.07 30.5 1 0

2 3300 0.526 6275.496 0.02 45.5 1.492 0.400

3 5400 0.911 5928.832 1 35 1.148 0.138

4 8300 2.991 2775.345 1 30 0.984 -0.017

5 12600 3.317 3798.535 1 110 3.607 1.283

6 15300 1.068 14323.776 1 39.5 1.295 0.259


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Summary output:

Regression Statistics Multiple R 0.57064 R Square 0.32563 Adjusted R Square -0.12395 Standard Error 0.515593 Observations 6

ANOVA df SS MS F



Error t Stat P-value Intercept -0.01228 0.375981 -0.03265 0.976004 x 1.93E-05 5.03E-05 0.384919 0.725965 U 0.143822 0.212773 0.675939 0.547522

RESIDUAL OUTPUT

Observation Predicted ln Residuals 1 -0.01228 0.012276 2 0.127195 0.272791 3 0.223185 -0.08556 4 0.578411 -0.59494 5 0.708549 0.574204 6 0.437341 -0.17877

9.6.4 Nitrites For the model, the independent variables were taken to be ‘x’ and ‘U’. The dependent variable was ‘ln’ which is the natural log of (N4/N4,0). The table is shown below.


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sample station

x U x/U AMMONIA NITRITE N4/N4,0 ln

1 0 0 0 0.07 0.06 1 0

2 3300 0.526 6275.496 0.02 0.06 1 0

3 5400 0.911 5928.832 1 0.44 7 1.946

4 8300 2.991 2775.345 1 0.10 1.6 0.470

5 12600 3.317 3798.535 1 0.15 2.4 0.875

6 15300 1.068 14323.776 1 0.23 3.6 1.281

Summary output:

Regression Statistics Multiple R 0.528417 R Square 0.279224 Adjusted R Square -0.20129 Standard Error 0.839073 Observations 6

ANOVA df SS MS F



Error t Stat P-value Intercept 0.328836 0.61187 0.537428 0.628278 x 8.5E-05 8.18E-05 1.038907 0.375225 U -0.13799 0.346266 -0.39852 0.716917

RESIDUAL OUTPUT


ln Residuals 1 0.328836 -0.32884 2 0.536688 -0.53669 3 0.662015 1.283895 4 0.621441 -0.15144 5 0.941785 -0.06632


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6 1.481552 -0.20062

9.6.5 Phosphates: For the model, the independent variables were taken to be ‘x’ and ‘U’. The dependent variable was ‘ln’ which is the natural log of (P/P,0). The table is shown below.


sample station

x U x/U PHOSPHATE P/P2,0 ln

1 0 0 0 0.10 1 02 3300 0.526 6275.496 0.10 1 03 5400 0.911 5928.832 1.00 9.986 2.3014 8300 2.991 2775.345 0.60 6.043 1.7995 12600 3.317 3798.535 0.63 6.300 1.8416 15300 1.068 14323.776 0.97 9.686 2.271

Summary output:


ANOVA df SS MS F



Error t Stat P-value Intercept 0.25538 0.646595 0.394962 0.719278 x 0.000131 8.64E-05 1.511144 0.227926 U 0.092408 0.365917 0.252538 0.81694


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RESIDUAL OUTPUT

Observation Predicted ln Residuals 1 0.25538 -0.25538 2 0.735002 -0.735 3 1.044866 1.25629 4 1.615841 0.183036 5 2.207651 -0.3671 6 2.352494 -0.08184

9.7 SISMOD OPERATION The operation of SISMOD, which was modified for the purposes of this study, has the following input requirements:

Model Input Requirements:

Essential Information

Essential information should only be entered once in following order:

Record 1

Description line

Record 2

Description line

Record 3

To the same line in the following order

• Number of stream reaches (positive integer)

• Number of headwaters (positive integer)

• Number of cross sections (positive integer)

• Number of point loads (positive integer)

• Status of nitrogen cycle modelling (integer, zero or one)

• Status of phosphorus cycle modelling (integer, zero or one)

If the status of the nitrogen cycle is entered as one; water quality variables organic nitrogen,


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ammonium nitrogen and nitrate nitrogen will be included into the simulation. Any number

different than one; will exclude them from the simulation. If the status of the phosphorus

cycle is entered as one; water quality variables organic phosphorus and phosphate phosphorus

will be included into the simulation. Any number different than one; will exclude them from

the simulation.

Reaches

These data should be entered in the following order:

Record 1

Description line

Record 2

Stream reach description (string – maximum 40 characters)

Record 3


• Stream reach no (positive integer)

• Stream reach type (positive integer; 1, 2 or 3)

• Cross section no (positive integer)

• Point source no (zero or positive integer)

• Downstream reach no (positive integer)

• Number of upstream reaches (positive integer)

Stream reach description can be any string that does not exceed 40 characters. Stream reach

no must start from 1 and be increased by one each time.

Stream reach type 1: Headwater reach

Stream reach type 2: Standard reach

Stream reach type 3: End reach

Entering zero for point source no means that no point sources are discharged into that reach.


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Record 4

Description line entered for the upstream reaches

Record 5

Upstream reach no (integer)

Record 1 should be entered only once. Record 2, Record 3 and Record 4 are entered once for

each stream reach. Record 5 is entered for each stream reach number of upstream reached

(Record 4) times.

If a stream reach is a headwater reach, number of upstream reaches should be entered as one

and the upstream reach should be negative of the headwater no that connects to the stream

reach.

Cross Sections

These data should be entered in the following order:

Record 1

Description line

Record 2

Cross section description (string – maximum 40 characters)

Record 3


• Cross section no (positive integer)

• Slope of the channel bottom (positive real number)

• Manning roughness coefficient (positive real number)

• Channel geometry (1, 2, 3 or 4)

Cross section description can be any string that does not exceed 40 characters. Cross section

no must start from one and be increased by one each time.

Channel geometry = 1: Symmetric triangle cross section

Channel geometry = 2: Rectangle cross section


109 E25-0177/05

Channel geometry = 3: Symmetric trapeze cross section

Channel geometry = 4: Irregular cross section

Record 4

Description line entered for the cross section geometry

Record 5


• Channel width B if necessary (in metres, positive real number)

• Chamfer slope Z if necessary (positive real number)

Rules to consider when entering Record 5:

• Only chamfer slope Z should be entered for symmetric triangular cross sections.

• Only bed width B should be entered for rectangular cross sections.

• For the symmetric trapezoidal cross sections first the bed width B and then the

chamfer slope Z should be entered

Record 6

Number points as x, y pairs that form the irregular cross section

Record 7

Description line

Record 8

x coordinate and y coordinate

modelling ngong river final project 2010

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