Assessing€Water€Quality€in€Developing … · 2011. 5. 27. · Assessing€Water€Quality€in€Developing€Countries:€A€Case€Study€in€TimorLeste ii From€TimorLeste:

Assessing Water Quality in DevelopingCountries: A Case Study in TimorLeste

Source: Alex Cullen 2004.

Halina Lamparski991359X

This dissertation is submitted as partial fulfilment of therequirements for the Degree of Bachelor of Engineering

(Environmental)

1 November 2004

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste i

ACKNOWLEDGEMENTS

A huge number of people have provided me with support and invaluable assistance with this

project:

From the Centre for Water Research, UWA:

Thanks firstly to my supervisor Dr Anya Waite who helped to get me involved in the Timor

Leste project, organising all the difficult and unexpected details involved with conducting a

research project in such unfamiliar territory and providing me with guidance along the way.

To Jill Birrell who was invaluable in helping me organise all my supplies and equipment for

the trip at such short notice, even though she knew her way around the laboratory less than

myself. To Dr Anas Ghadouani for providing me with moral support and guidance in ways

both academic and nonacademic. To the Administrative Staff (Wendy, Julia and Ros) and

Computer support team for all their assistance. To Alex Wyatt, my fellow CWR buddy who

kept me company through all the ups and downs of the trip. To all my design and thesis class

mates for their great value and good times during this crazy final year of university.

From Plant Biology, UWA:

Thanks must go to Dr Grey Coupland who accompanied me on the trip to TimorLeste and

was the primary force in organising my field sampling design and looking after me when I

was sick.

From the Department of Geology & Geography, UWA:

To Dr Myra Keep who was so dedicated in organising this huge project, culminating in the

visit by TimorLeste’s primeminister, Dr Mari Alkatiri to UWA. I am extremely grateful for

having had such an amazing experience in TimorLeste. Dr Warwick Crowe was the logistics

king in TimorLeste. Thankyou for driving back and forth to Dili from Samé trying to get

those chemicals, teaching me card games, having great taste in music and being fantastic fun

while over there. To Dr David Haig, and the rest of the geology and greography crew on the

trip: Alex (especially for his brilliant photography), Pyone, Nina, Logan and Eujay. Sharing

the house with you in Samé made the trip so much fun!

From the University of Melbourne:

To Kate Harper and Dr Mike Sandiford for their advice in TimorLeste and all their card

playing antics.

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste ii

From TimorLeste:

All those involved with the Department of Energy and Mineral Resources: Gaspar, Jamie,

Lourenco, Francisco, Brasildo and Mr Izidio. Your warm welcome and friendship is

something I will not forget. Thankyou especially to Gaspar and Jamie for all their help with

collecting my data. To Augusto Pinto from the Department of Environmental Services

thankyou for providing me with your data and showing me around Dili. To Steve Walsh

(Australia) and Matz Ljungwald (Sweden) from the UN Police. It was great to meet both of

you in Samé and thankyou for your friendship and time. To the kids from Dili Polytech: Lou,

Johnnie, John, Ele (my special water quality friend), Ambere and Ponte. I will not forget you.

And a special thankyou must go to all the local Timorese who supported us crazy Malai,

obrigado barak!

To my darling friends who are so delightful: Jacques, Helen, Danny, Clint, Fish, Spacka,

Jono, Penny, Deepak, Dylan and the rest who I love just as much but don’t get to see as often.

Especially to Tristan who has continually challenged me and supported me these last years,

thankyou darling.

And to all my family in Australia and Poland, especially my amazing parents Marek and

Barbara and little sister Joasia who have provided me with their unconditional love through

this difficult year.

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste iii

ABSTRACT

Water quality assessments are an essential procedure in monitoring programs and are used to

collect baseline environmental data. They are particularly important in developing regions

where people often cannot access adequate supplies of water and effective water resource

management is critical for future development.

TimorLeste is a nation in such a position. Very little water quality data on Timorese

catchments is currently available. As part of a larger project in TimorLeste, the first water

quality assessment of the Samé river system was conducted in July 2004. Basic physical,

chemical and biological analyses were performed on water samples collected during a three

week field trip to the area. Temperature, pH and salinity were measured in situ, while nutrient,

metal and chlorophyll a concentrations were determined from test strip analyses and

fluorometric techniques, respectively. The major aims of the study were to collect baseline

data for the region and more specifically, to assess the water quality status along the Samé

river system on both a regional scale and local scale. A secondary aim was to assess the

effectiveness and appropriateness of water quality measuring techniques for TimorLeste, as

an example of a developing nation.

From results it appeared that the physical characteristics of the Samé river system was

generally representative of typical rivers in the region during the dry season: with neutral pH

and low salinity levels. Nutrient inputs, however, were found to be significantly lower than

those of other rivers in Indonesia. This suggested a relatively pristine oligotrophic

environment contributing minimal nutrient loads to the coast. Relatively low chlorophyll a

concentrations (ranging between 0.0152.04 g/L) confirmed this finding. No statistically

significant linear relationship was observed between chlorophyll a levels and distance

downstream, as might have been expected due to the cumulative effects of nutrients.

However, a positive linear relationship was found between these two parameters at a local

scale in the Samé town drains. This suggested that even though drain water contained

relatively low chlorophyll a levels, in the same range as the river system, the cumulative

impact of human activity in the town may have resulted in increasing nutrient inputs

promoting algal growth.

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste iv

It was concluded that the test strip analyses was a cheap, effective method for observing

significant nutrient loads in rivers, while fluorometric chlorophyll a analysis was a better

technique for investigating changes in water quality in more pristine environments. Such

techniques were recommended to continue monitoring over a variety of temporal and spatial

scales in the Samé river system. In this way any changes in water quality might be more

effectively investigated, especially in the context of future development which might impact

upon the pristine Samé riverine system.

Table of Contents

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste v

TABLE OF CONTENTS

ACKNOWLEDGEMENTS..........................................................................................................IABSTRACT .................................................................................................................................III

TABLE OF CONTENTS.............................................................................................................VLIST OF FIGURES.................................................................................................................. VIILIST OF TABLES...................................................................................................................VIII1 INTRODUCTION ................................................................................................................ 9

1.1 PROJECT AIMS ................................................................................................................111.2 DISSERTATION STRUCTURE ...........................................................................................11

2 LITERATURE REVIEW.................................................................................................. 122.1 WATER QUALITY IN SOUTH EAST ASIA ........................................................................12

2.1.1 Motivation for Research ...................................................................................... 122.1.2 Previous Studies: Types of Assessment............................................................... 132.1.3 Previous Studies: Trends in Water Quality........................................................ 16

2.2 MOTIVATION FOR THE STUDY: WATER QUALITY RESEARCH IN TIMORLESTE ..........17

3 METHODOLOGY ............................................................................................................. 183.1 THE TIMOR PROJECT ......................................................................................................183.2 STUDY SITE: A BACKGROUND TO TIMORLESTE .........................................................19

3.2.1 Regional Climate and Geography....................................................................... 203.2.2 Political and Social History ................................................................................ 21

3.3 FIELD TRIP SITE..............................................................................................................233.3.1 Living Conditions................................................................................................. 243.3.2 Weather Conditions ............................................................................................. 253.3.3 Land Use in the Study Site................................................................................... 25

3.4 GOALS AND LIMITATIONS ..............................................................................................263.5 LOGISTICS OF ANALYSING WATER QUALITY IN A DEVELOPING NATION....................27

3.5.1 Taking Initiatives.................................................................................................. 273.5.2 Accessing Materials............................................................................................. 28

3.6 SAMPLING DESIGN .........................................................................................................293.6.1 Field Sampling ..................................................................................................... 313.6.2 Water Quality Parameters................................................................................... 32

3.7 PHYSICAL PARAMETERS.................................................................................................333.7.1 Temperature, Salinity and pH............................................................................. 333.7.2 Flow Rate.............................................................................................................. 34

3.8 CHEMICAL PARAMETERS ...............................................................................................353.8.1 Nutrients ............................................................................................................... 373.8.2 Metals.................................................................................................................... 38

3.9 BIOLOGICAL PARAMETERS ............................................................................................383.9.1 Chlorophyll a........................................................................................................ 38

3.10 SOURCE OF ERRORS........................................................................................................403.10.1 Sampling Error..................................................................................................... 403.10.2 Error in Physical Measurements ........................................................................ 413.10.3 Error in Chemical Measurements....................................................................... 413.10.4 Error in Biological Measurements ..................................................................... 41

Table of Contents

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste vi

4 RESULTS............................................................................................................................. 434.1 PHYSICAL DATA .............................................................................................................43

4.1.1 Temperature ......................................................................................................... 434.1.2 pH.......................................................................................................................... 444.1.3 Salinity .................................................................................................................. 454.1.4 Flow Rates............................................................................................................ 46

4.2 CHEMICAL DATA............................................................................................................464.3 BIOLOGICAL DATA .........................................................................................................48

4.3.1 Chlorophyll a........................................................................................................ 485 DISCUSSION ...................................................................................................................... 51

5.1 REGIONAL SIMILARITIES ................................................................................................515.2 NUTRIENT INPUTS AND RIVERINE DELIVERY TO THE COAST .......................................525.3 LOCAL WATER QUALITY ...............................................................................................545.4 ASSESSMENT OF METHODOLOGY ..................................................................................54

6 CONCLUSIONS................................................................................................................. 58

7 RECOMMENDATIONS ................................................................................................... 598 REFERENCES.................................................................................................................... 619 APPENDICES..................................................................................................................... 66

APPENDIX A: GLOBAL POSITIONING SYSTEM (GPS) DATA .....................................................66APPENDIX B: RAW PHYSICAL DATA..........................................................................................67APPENDIX C: RAW CHEMICAL DATA ........................................................................................70APPENDIX D: RAW BIOLOGICAL DATA .....................................................................................74APPENDIX E: SAMPLE SITE NOTES, OBSERVATIONS AND PHOTOGRAPHY...............................76APPENDIX F: LIST OF EQUIPMENT USED DURING THE FIELD TRIP IN TIMORLESTE...............87

List of Figures

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste vii

LIST OF FIGURES

FIGURE 31: GLOBE MAP ILLUSTRATING THE LOCATION OF EAST TIMOR IN RELATION TOINDONESIA AND AUSTRALIA. SOURCE: (EAST TIMOR ACTION NETWORK/US 2004B) .......19

FIGURE 32: MAP OF TIMORLESTE. SOURCE: (U.S. CENTRAL INTELLIGENCE AGENCY 2003) .21FIGURE 33: MAP SHOWING SAMPLING REGION IN TIMORLESTE. SOURCE: (U.S. CENTRAL

INTELLIGENCE AGENCY 2003)...............................................................................................24FIGURE 34: A PHOTO OF THE HOUSE RENTED IN SAMÉ DURING THE FIELD TRIP. THE MAKESHIFT

LABORATORY IS LOCATED TO THE LEFT OF THE HOUSE. SOURCE: ALEX CULLEN 2004. .....25FIGURE 35: A PHOTO OF THE WATER FACILITIES AVAILABLE AT THE HOUSE IN SAMÉ. SOURCE:

ALEX CULLEN 2004................................................................................................................25FIGURE 36: A PHOTO OF A LOCAL CORN CROP, SAMÉ, TIMORLESTE. SOURCE: ALEX CULLEN

2004. .......................................................................................................................................26FIGURE 37: A PHOTO OF A TRADITIONAL TIMORESE HOUSE, SAMÉ, TIMORLESTE. SOURCE:

ALEX CULLEN 2004................................................................................................................26FIGURE 38: TOPOGRAPHICAL MAP OF SOUTH COAST OF TIMORLESTE SHOWING SAMPLING

LOCATIONS R1R24. SOURCE: (DIBUAT DAN DITERBITKAN OLEM 1993B; DIBUAT DANDITERBITKAN OLEM 1993A) ..................................................................................................30

FIGURE 39: TAKING MEASUREMENTS FROM DRAINS RUNNING DOWN THE SIDE OF THE ROAD INTHE TOWN OF SAMÉ, TIMORLESTE. SOURCE: DR GREY COUPLAND 2004. ........................31

FIGURE 310: GASPAR DA COSTA DA JESUS (ERM) TAKING IN SITU PHYISCAL MEASUREMENTSUSING THE YEOCAL PROBE. SOURCE: DR GREY COUPLAND 2004. ...................................34

FIGURE 311: TAKING A HAND MEASUREMENT OF RIVER WIDTH TO CALCULATE VOLUME FLOWRATE. SOURCE: DR GREY COUPLAND 2004. .........................................................................34

FIGURE 312: AMMONIUM AND PHOSPHATE TEST STRIPS. SOURCE: ALEX WYATT 2004. .........37FIGURE 313: USING TEST STRIPS FOR SEMIQUANTITATIVE ANALYSES OF NUTRIENTS AND

METALS. SOURCE: ALEX WYATT 2004. ................................................................................37FIGURE 41: AVERAGE TEMPERATURE IN SAME, CILIWUNG AND SUNTER RIVERS. DATA FOR THE

CILIWUNG AND SUNTER SYSTEMS FROM PALUPI ET AL. (1995). ..........................................44FIGURE 42: AVERAGE PH OF THE SAME, CILIWUNG AND SUNTER RIVER SYSTEMS. THE

CILIWUNG AND SUNTER RIVERS ARE LOCATED IN JAKARTA, INDONESIA. DATA FOR THESESYSTEMS IS SOURCED FROM PALUPI ET AL. (1995). ..............................................................45

FIGURE 43: AVERAGE ELECTRICAL CONDUTIVITY OF THE SAMÉ, SAGULING AND MARO RIVERSYSTEMS, STANDARDISED TO 25°C. DATA FOR THE SAGULING SYSTEM SOURCED FROMHART ET AL. (2002) AND FOR THE MARO SYSTEM SOURCED FROM SULISTYAWAN &HARTONO (2002). ...................................................................................................................46

FIGURE 44: CHLOROPHYLL A CONCENTRATION VERSUS LONGITUDINAL DISTANCE FROM EACHSAMPLE SITE (R1R24) IN THE SAMÉ RIVER SYSTEM. ...........................................................49

FIGURE 45: CHLOROPHYLL A CONCENTRATION VERSUS RIVER FLOW RATE IN THE SAMÉSYSTEM....................................................................................................................................49

FIGURE 46: CHLOROPHYLL A CONCENTRATION VERSUS DISTANCE FROM FIRST SAMPLELOCATION IN THE SAMÉ TOWN DRAIN....................................................................................50

List of Tables

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste viii

LIST OF TABLES

TABLE 21: CLASSIFICATION OF RIVERS BASED ON THE WATER QUALITY INDEX (WQI) USEDFOR MAJOR RIVERS IN MALAYSIA (ONG ET AL. 1987). ..........................................................14

TABLE 22: CLASSIFICATION OF RIVERS BASED ON THE WATER QUALITY INDEX (WQI) USEDFOR RIVERS NEAR JARKARTA, INDONESIA (CANTER 1985). .................................................15

TABLE 23: A COMPARISON OF WATER QUALITY PARAMETERS IN SOME RIVERS IN SOUTH EASTASIA DATA FOR THE SAGULING RESERVOIR SOURCED FROM HART ET AL. (2002). DATA FORTHE CILIWUNG AND SUNTER RIVER SYSTEMS FROM PALUPI ET AL. (1995) DATA FOR THEBANTIMURUNG RIVER SOURCED FROM WHITTEN ET AL.(1987). DATA FOR THE SINGAPORERIVER AND SUNGEI GEYLAND IS SOURCED FROM SIEN AND HUAY (1987)..........................16

TABLE 31: METHODOLOGICAL LIMITATION AND INITIATIVES TAKEN TO COUNTERACT THESEDURING THE FIELD OPERATION IN TIMORLESTE...................................................................27

TABLE 32: PARAMETERS CHOSEN FOR ASSESSING WATER QUALITY IN TIMORLESTE. ..............33TABLE 41: TEMPERATURE, PH, SALINITY AND FLOW RATES OF THE SAMÉ RIVER SYSTEM........43TABLE 42: AVERAGE NUTRIENT AND METAL CONCENTRATIONS OF THE SAME, CILIWUNG,

SUNTER AND SAGULING RIVER SYSTEMS IN THE DRY SEASON. DATA FOR THE CILIWUNGAND SUNTER SYSTEMS IS SOURCED FROM PALUPI ET AL. (1995) AND FOR THE SAGULINGSYSTEM IS SOURCED FROM HART ET AL. (2002)....................................................................47

TABLE 51: A COMPARISON OF THE COST, PRACTICALITY, SIMPLICITY, WASTE DISPOSALREQUIREMENTS AND ANALYTICAL RESOLUTION OF WATER QUALITY ASSESSMENTTECHNIQUES............................................................................................................................55

TABLE 91: COORDINATES OF EACH SAMPLE SITE (R1R24) ........................................................66

1 Introduction

Assessing Water Quality in Developing Countries: A Case Study in TimorLeste 9

1 INTRODUCTION

Water is essential for the survival of all life. Humans depend on water to fulfill a variety of

needs and wants. These include water for drinking, health and sanitation, recreation and as

part of spiritual and cultural traditions. It is a resource that is often taken for granted, yet a

large proportion of the world’s population is facing a serious water crisis. Presently, more

than 1 billion people lack access to clean drinking water and over 2.4 billion lack access to

proper sanitary facilities (2003). The mismanagement of water resources has led to an

increase in their degradation. According to Agarwal & Narain (2004) it is the world’s poor

that are most affected by “ecological poverty” in the form of inadequate supplies of good

quality water. Therefore improving management of water resources in poorer, developing

countries is essential for the enhancement of quality of life and for further development in

these regions.

Assessing the quality of water resources is an essential process in the development of water

resources. Water quality may be defined in terms of specific characteristics of water that are

important with regards to a certain service (Tchobanoglous and Schroeder 1987). These

characteristics are usually defined as physical, chemical and biological parameters. Examples

are, heavy metal concentrations in a river intended for drinking water or levels of dissolved

oxygen in a lake used for fishing.

Water quality may be assessed by a number of various techniques, ranging in complexity and

sophistication. When planning to conduct any scientific assessment it is usual to consider

what techniques are most appropriate with respect to: how expensive it is to perform

(including both the setup and repetition of the technique); how simple it is to conduct and

thus how much error may be incurred in the process; and how practical it is to employ the

technique. Such considerations are especially necessary in regions which lack infrastructure.

For example, access to a constant power supply, refrigeration, vehicles for field work, basic

scientific equipment or facilities to dispose of chemical waste safely. Therefore, the methods

chosen for work in developing countries must take these factors into account. It is also,

however, important to ensure that techniques provide data of adequate resolution and

accuracy to be useful in an assessment. Thus compromise between these factors is often

necessary (Hussain 1978).

1 Introduction


TimorLeste (also known as East Timor) is a nation that is currently struggling to supply

water of adequate volume and quality for human use. It is located on the eastern half of the

island of Timor and is approximately 700km northwest of Darwin. TimorLeste has only

recently seen the end of 25 years of Indonesian occupation, after over 400 years of Portuguese

rule (East Timor Action Network/US 2004b; Hiorth 1985) The social and political instability

following the country’s vote for independence in 1999 has left TimorLeste struggling to

rebuild the infrastructure that was destroyed during this period. The lack of infrastructure and

knowledge necessary for water resource management is only one of many important issues

that the Timorese are currently facing (United Nations Economic and Social Commission for

Asia and the Pacific 2002).

This study was included as part of the larger project with goal of collecting simple baseline

data on the state of the river system in the southern region of Samé, in TimorLeste. Baseline

data may be characterised as data collected for future comparison, as part of a monitoring

program for impact assessment. In the context of this study, baseline water quality data will

be valuable when monitoring changes in the health of the river systems from impacts of future

developments in the region.

A 3 week field trip to TimorLeste was conducted from the 21st June to the 14th July 2004.

The expedition involved a large contingent of UWA staff and students, staff from the

Department of Energy and Mineral Resources (ERM) and the Department of Environmental

Services in Dili, civil engineering students from the Dili Polytech University as well as

support from staff of the United Nations (UN) Police and many local Timorese. The

government staff and engineering students were partnered with UWA students to actively

train in the scientific methods that were employed during the field trip, as well as assist UWA

students in collecting data for their projects.

The lack of available data concerning TimorLeste’s natural environment highlights the

significance of this project. It will result in the collection of some of the first environmental

data in the region. More specifically, the data will provide information on how land use in

TimorLeste might impact upon riverine water quality, and the effects on regional water

supplies and the coastal environment.

1 Introduction


1.1 Project Aims

The overall aim of the project was to contribute baseline environmental data to the

government of TimorLeste on its natural resources.

In the case of this study, the specific aims were to:

a. Assess the water quality status of the Samé river system by measuring key

parameters along the mountaincoast gradient on both a regional scale and local

scale.

b. Assess the effectiveness and appropriateness of water quality measuring techniques

for TimorLeste, as an example of a developing nation.

1.2 Dissertation Structure

The issues and objectives outlined above are explored in further detail in the remainder of this

dissertation. Chapter 2 presents a critical review of the literature concerning previous water

quality assessments in the region of south east Asia and how both techniques and results from

different studies compare. Chapter 3 provides information on the origin and structure of the

greater project that this study was a part of, and presents the motivation for the study in more

detail. It also provides a background on both the physical and social environment of Timor

Leste with a focus on the study site. A detailed description and discussion of both the

planning and execution of the methods used for field work in TimorLeste is also presented.

Results from the field trip are displayed in Chapter 4. Chapter 5 includes an analysis of these

results and of the success of the methodologies used during the field trip. Chapters 6 and 7

present conclusions and recommendations for further work based upon these analyses.

2 Literature Review


2 LITERATURE REVIEW

The quality of inland waters in south east Asia is both similar and varied across the region. A

number of studies have been undertaken assessing the water quality of rivers. This chapter

presents a critical review of the objectives, methods and trends found in water quality

assessments conducted in southeast Asia. The motivation for this study is also explained.

2.1 Water Quality in South East Asia

2.1.1 Motivation for Research

Various water quality studies have been conducted throughout southeast Asia. The

motivation for undertaking such studies is similar from country to country. Inadequate

supplies of good quality water has been recognised as one of the major constraints to future

development in south east Asia, as is in the case in Indonesia (The World Bank 1995).

Therefore, water quality studies are necessary to continually assess and effectively

management water supplies. A major motivation for such research on the quality of rivers in

south east Asia is the lack of baseline data for the region. Baseline data is crucial for adequate

assessment of changes in water quality, as part of effective water management planning.

Pollution of reservoirs that serve as locations of both domestic and industrial effluent and

runoff and as receiving bodies is also a common motivation for water quality studies and

improvement programs (Papista et al. 2002; Hart et al. 2001; Kao et al. 1978). The high

quality of water discharged from forested watersheds is well known and increasing

deforestation of tropical forests in south east Asia has been found to worsen incoming riverine

pollution (Food and Agriculture Organisation of the United Nations Forestry Department

2003). Riparian forests improve dissolved oxygen levels in water by maintain cooler water

temperatures (Brooks et al. 1997). Thus forests help to decrease nutrient release into the water

column and also cycle nutrients and chemicals reducing nutrient pollutants and some heavy

metals. They also help to stabilise stream banks and reduce runoff which may contain

pollution into water bodies from upland areas (Brooks et al. 1997). Some of the major issues

related to pollution of tropical rivers include; the quality of drinking water, excessive growth

of floating plants, algal blooms, fishkills and organic and heavy metal contamination (Palupi

et al. 1995; Manan and Ibrahim 2003; Hart et al. 2002). As a result of excess nutrient inputs,

rivers are also responsible for the pollution of coastal waters in south east Asia (Sien and

2 Literature Review


Huay 1987). The delivery of nutrients to the ocean can cause severe algal blooms and impact

upon the balance of the ocean ecology (Mann 2000). By assessing water quality it is possible

to gain further understanding of the dynamics such phenomenon and so improve water

pollution control in south east Asia (Khare 1978).

2.1.2 Previous Studies: Types of Assessment

A suite of water quality assessments have been conducted in various regions of southeast

Asia. These range from nutrient bases assessments (Hart et al. 2002), index classification

(Ong et al. 1987; Palupi et al. 1995), communitybased river management programs (Manan

and Ibrahim 2003) and bioassessments in conjunction with predictive models (Hart et al.

2001). The most common assessments were found to be the measurements of basic physical

parameters (such as pH, temperature, dissolved oxygen and conductivity) and chemical

parameters, specifically nutrient concentrations (such as total nitrogen and phosphorus). This

are demonstrated by basic physical and chemical measurements of water quality in Indonesian

(Ong et al. 1987; Whitten et al. 1987), Malaysian (Ong et al. 1987), Singaporean (Sien and

Huay 1987) and Taiwanese (Kao et al. 1978) rivers. Hart et al. (2001) recognises that

physical and chemical methods are mostly used to assess water quality while the assessment

of river health using biological methods appeared more uncommon. A critical review of the

different methodologies used to assess water quality is presented below.

Nutrient Budgets

Nutrients are a major pollutant of natural water ways (Wetzel 2001). Excessive nutrients can

result in major water quality problems in tropical regions, including uncontrollable growths of

floating plants, toxic cyanobacterial blooms and regular fishkills (Hart et al. 2002). Such

problems occur in the Citarum River basin in Java, Indonesia, where large volumes of

untreated domestic and industrial effluent are released. A nutrient budget was determined to

quantify the behaviour of nutrients entering the Saguling reservoir in this basin (Hart et al.

2002). Totalphosphorus, filterable reactive phosphorus (FRP), total nitrogen, NH4N and

NOxN were analysed in water samples taken from five sites in the Saguling. The nutrient

budget was focused on phosphorus and nitrogen because they were considered to be the main

contributors to the excessive plant growth and cyanobacterial problems observed in the

Saguling (Hart et al. 2002). This link was also found by .pH, conductivity and dissolved

oxygen (DO) were also monitored in situ. These physical parameters were measured in

2 Literature Review


addition to the nutrients because of their affect on the presence and state of nutrients (Hart et

al. 2002).

Water Quality Indexes (WQI)

Water quality monitoring programs have been established for major river basins in Malaysia.

Physical, chemical and biological parameters are measured both in situ and in a laboratory.

The status of water quality of major rivers in Malaysia is defined by a Water Quality Index

System (WQI) scaled from zero to a hundred (Ong et al. 1987). The higher the number of the

WQI, the better the quality of the river water. River water quality is labelled according to the

system presented in Table 21. The reasoning for this particular classification system is not

explained by Ong et al. (1987).

Table 21: Classification of Rivers based on the Water Quality Index (WQI) used for major rivers inMalaysia (Ong et al. 1987).

Water Quality Index Description

> 80 Clean

59 – 79 Slightly Polluted

33 – 58 Moderately Polluted

< 32 Heavily Polluted

This is a similar analytical system to that used by Palupi et al. (1995) who also define water

quality using a WQI. While Ong et al. (1987) define a WQI using 5 parameters: Biological

Oxygen Demand (BOD), AmmoniaNitrogen (NH3N), Suspended Solids (SS) and acidity

(pH), Palupi et al. (1995) base their index on 9 parameters. pH, BOD, total solids and nitrate

concentration are shared measurements, while Palupi et al. (1995) also include temperature,

dissolved oxygen, faecal coliform concentration, phosphate concentration and turbidity in

their WQI. The classification of river water quality based on final index numbers was

suggested by Canter (1985) and differs in scale and description compared to that used by Ong

et al. (1987). This classification system is presented in Table 11 below.

2 Literature Review


Table 22: Classification of Rivers based on the Water Quality Index (WQI) used for rivers near Jakarta,Indonesia (Canter 1985).

Water Quality Index Description

0 – 25 Very bad/poor

26 – 50 Bad/poor

51 – 70 Medium

71 – 90 Good

91 100 Excellent

Bioassessments

The biological health of a river catchment can be used as an indicator of water quality

(Tchobanoglous and Schroeder 1987). Assessment of river health using biological methods is

uncommon in developing countries, where physical and chemical methods are mostly used to

assess water quality (Hart et al. 2001).Bioassessment was used to assess the ecological

integrity of 15 sites in the upper Brantas River catchment, Indonesia (Sudaryanti et al. 2001).

A number of reference sites were selected in riffle habitats. The biological status of the river

system was determined by sampling for and identifying macroinvertebrates. This was

conducted by local biologists following intensive training and supervision. The

macroinvertebrate data were used to develop a predictive model (titled AUSRIVAS) for the

uppermiddle Brantas river, which was then used to assess the “health” of the test sites in the

catchment (Sudaryanti et al. 2001). A suite of “monitoring” environmental variables were

also measured at each site including: water temperature, conductivity, pH, DO, total P and

total N. These variables are common to other water quality studies in the region (Palupi et al.

1995; Ong et al. 1987; Manan and Ibrahim 2003), in contrast to the method of bioassessment

itself .These were judged to be potentially influenced by human impacts in the catchment. The

AUSRIVAS model was assessed as being a rapid bioassessment method that was highly

applicable to the uppermiddle catchment sections of Indonesian river systems. In the

bioassessment of the Brantas river, almost all the sites were classified as “significantly

impacted” in comparison to the reference site.

2 Literature Review


2.1.3 Previous Studies: Trends in Water Quality

The water quality status appeared generally homogeneous across riverine systems in south

east Asia. A comparison of some of the results of previous studies is presented in Table 23

below.

Table 23: A comparison of water quality parameters in some rivers in south east Asia Data for theSaguling reservoir sourced from Hart et al. (2002). Data for the Ciliwung and Sunter river systems from

Palupi et al. (1995) Data for the Bantimurung river sourced from Whitten et al.(1987). Data for theSingapore River and Sungei Geyland is sourced from Sien and Huay (1987).

River System pH Cond.S/cm)

DO(mg/l)

Temp.(°C)

Total P(mg/l)

Total N(mg/l)

FRP(mg/l)

NO3N(mg/l)

NH4N(mg/l)

Saguling,Indonesia

6.38.4

126367 0.9–8.4 23.223.9 1.22 2.39 0.142 0.572 0.213

Ciliwung,Indonesia

7.3 1.31 27.5 1.39

Sunter,Indonesia

7.4 1.01 27.8 0.81

Bantimurung,Indonesia

6.57.5 2629

Singapore 1.8SungeiGaylang,Singapore

3.3

ChiSui,Taiwan

2.08.8 0.31.4 0.100.69

The pH levels in three of the Indonesian rivers were similarly neutral, while the DO levels of

the Sunter and Ciliwung rivers were relatively low (Table 23) compared to those of the

Saguling, Bantimurung and ChiSui rivers which could reach concentration greater than

7mg/l. Surface temperatures did not vary distinctly, however, it is difficult to compare as

temperature may have been affected by the surroundings around them, such as the presence of

trees that might shade and reduce surface temperatures in rivers. The total N levels of the

Saguling were over double that measured in the ChiSui river in Taiwan, while the Nitrate

concentrations of the Saguling were approximately half of that of the Ciliwung and Sunter

rivers. The concentration of ammonium in the Saguling was also significantly lower than that

of Singapore rivers, by one order of magnitude.

It is interesting to compare data for rivers in south east Asia to the trigger levels (used to

assess risk of adverse effects due to nutrients, biodegradable organic matter and pH in various

ecosystem types) of tropical aquatic ecosystems defined by the ANZECC/ARMCANZ

2 Literature Review


guidelines (2000). The pH range for rivers is defined as 6.08.0. This criterion is fulfilled in

the rivers presented in Table 23. The total nitrogen trigger value is defined as between 150

300 g/l (=0.150.3 mg/l). This criterion is exceeded by both rivers presenting total nitrogen

data, the Saguling and ChiSui, by approximately 1 order of magnitude. Nitrate and

ammonium trigger levels are defined by the ANZECC/ARMCANZ (2000) guidelines as

between 1030 g/l (= 0.010.03 mg/l) and 610 g/l (= 0.0060.01 mg/l), respectively. The

nitrate levels of the Saguling, Ciliwung and Sunter exceed the criteria by two and three orders

of magnitude, while the Saguling, Singapore and Sungei Gaylang exceed ammonium trigger

levels by up to four orders of magnitude. In summary, these nutrient trends indicate high

levels of pollution in many rivers in southeast Asia. This is confirmed by Hart et al.; Palupi

et al. (1995); Whitten et al (1987); Sien and Huay (1987) who link human impacts in the form

of domestic, industrial and agricultural effluents as sources of nutrient pollution in these

rivers.

2.2 Motivation for the Study: Water Quality Research in TimorLeste

As investigated previously, a variety of water quality studies have been conducted in south

east Asia. However, almost no literature has been published describing the water quality of

inland water bodies in TimorLeste itself. It is possible that such information was destroyed

during the recent periods of instability in the nation. From communication with local

Timorese and members of NonGovernment Organisations, faecal coliform measurements are

being conducted in water bodies in the enclave of Oecussi by an NGO entitled Ozgreen

(Ozgreen 2004). Water quality assessments have also been performed by a Portuguese water

bottling company in the mountainous region surrounding Samé. Only one environmental

organisation exists in TimorLeste: the Haburas Foundation (East Timor Action Network/US

2004a). However, information regarding any environmental monitoring that it has conducted

was also difficult to discover. Some physical data has also been collected by the Department

of Environmental Services however this has not been published (Augusto Pinto, pers. comm..,

23/06/2004). In summary, very little environmental data has been published, especially

concerning the quality of TimorLeste’s inland waters. This lack of information is the major

motivation for this study. Any results obtained from this study will be one of the first water

quality data sets for TimorLeste.

3 Methodology


3 METHODOLOGY

Chapter 4 details the origins of this study as part of the larger project with which it was

associated. Background information regarding the arrangement of the field trip in Timor

Leste are also provided. The physical geography, climate, and social history and geography of

TimorLeste and the local study site are also summarised. The chapter reveals the goals and

limitations of sampling and analytical techniques when used in a developing country. The

steps of each sampling and analytical process are described in detail and explained. The

potential sources of error for the methodology conducted in TimorLeste are also discussed.

3.1 The Timor Project

This research was conducted as part of a larger project incorporating seven geology,

geography and environmental engineering final year projects from UWA, and a geology

doctorate from the University of Melbourne. Dr Myra Keep (Geology and Geography

Department, UWA) was responsible for initiating interest and funding for the project, and

coordinating the involvement of government staff from the Department of Energy and

Mineral Resources (EMR) and Department of Environmental Services in Dili. Civil

engineering students from Dili Polytech University were also invited to work alongside the

Australian students on their individual projects. Dr Warwick Crowe (Geology and Geography

Department, UWA) was involved in organising the logistics of the field operation, in

coordination with both Timorese government staff and students.

Each of the Timorese students and government staff were partnered with an Australian

student and actively engaged in field work contributing to individual projects. Such a

partnership was designed to provide training to both staff and students in various scientific

methods. The exercise also provided the Timorese students with an opportunity to present

their experiences and knowledge to their own university. By working alongside the Timorese,

Australian students gained invaluable assistance because of their local knowledge and ability

to translate their intentions to local communities.

UWA students were provided with a two day intensive language course in the official

Timorese language of Tetum, sponsored by Woodside Petroleum, and a St John’s Senior First

Aid course before traveling to TimorLeste. Students travelled to and from Dili (via Darwin)

as visitors on chartered ConocoPhillips Pty Ltd flights. In Dili, individual projects were

3 Methodology


formally presented to the government of TimorLeste. A presentation summarising

preliminary findings from the field operation was also made to both the government and Dili

Polytech University, before returning to Australia.

3.2 Study Site: A Background to TimorLeste

Field research for this study was conducted in the country of TimorLeste, more commonly

known as East Timor. TimorLeste is located on the eastern half of the island of Timor in

southeast Asia. It includes a small area in the western half of the island around the enclave of

OecussiAmbeno, as well as the small island of Jaco and Atauro, and 30km north of Dili, the

capital. TimorLeste covers an area of approximately 15 000 square kilometres (Hiorth 1985).

It is situated roughly 700km northwest of Darwin and is separated from Australia by the

Timor Sea (Heyward et al. 1997). The position of TimorLeste on a global scale is represented

in Figure 31 below.

Figure 31: Globe map illustrating the location of East Timor in relation to Indonesia and Australia.Source: (East Timor Action Network/US 2004b)

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3.2.1 Regional Climate and Geography

The climate in this region of southeast Asia is tropical with two distinct seasons: the

Northwest monsoon from November to March, associated with high rainfall and tropical

cyclones and thunderstorms (the “wet” season), and the Southeast monsoon from April to

September associated with low rainfall (the “dry” season) (Heyward et al. 1997). The north

coast receives virtually no rain during this period and rainfall is often erratic.

The average air temperature of the region is approximately 28°C with the highest humidities

occurring from October to May, associated with the Northwest Monsoon season. Lower

humidities occur during the Southeast Monsoon season, due to the continental origin of the air

mass (Heyward et al. 1997).

Two distinct wind regimes are associated with the monsoonal seasons. A steady south

easterly airflow, originating over the Australian mainland, is present during the Southeast

monsoon. A steady, moist, west to northwest wind occurs in TimorLeste during the summer

months of the Northwest monsoon (Heyward et al. 1997).

Once part of the Australian continental shelf, Timor only fully emerged from the ocean some

4 million years ago, and is therefore comprised mainly of marine sediment, principally

limestone. The rugged Kablaki mountain range runs directly eastwest through the centre of

the island of Timor dividing it lengthwise. It is often cooler and receives more rain than the

northern half of the island during the dry season. The major riverine systems of TimorLeste

originate in these mountains and extend towards the coasts as swamps and river deltas, mostly

along the south coast (Hiorth 1985).

Tropical and subtropical forests cover less than 50% of TimorLeste’s surface area (Bouma

and Kobryn 2002). Sandalwood and Teak forests are some of the more marketable forests that

grow in Timor; however, these were greatly diminished during Portuguese and Indonesian

occupation. A majority of the nation’s forest cover exists in the mountain range, however,

continual deforestation as a result of fuel wood collection, livestock grazing and agricultural

use is decreasing forested areas (Bouma and Kobryn 2002).

Agriculture is the single largest land use in TimorLeste (approximately 24% of the total

area). Subsistence farming and rotational cropping is relied upon by the majority of rural

communities in TimorLeste (Bouma and Kobryn 2002). Erratic rainfall patterns often

3 Methodology


contribute to unfavorable agricultural conditions. An increasing population has resulted in

increased competition for resources and intensified land use. This has brought about a decline

in the area of forest and woodland.

3.2.2 Political and Social History

The capital city of TimorLeste is Dili and the second major town is Baucau, situated further

east of Dili (Figure 32). The population of TimorLeste is approximately 1 million (U.S.

Central Intelligence Agency 2004). The official languages of TimorLeste are Portuguese and

Tetum, although BahasaIndonesian is also a commonly spoken language. TimorLeste is also

known as Timor Loro’sae, or the land of the rising sun in Tetum. The nation is governed

using a parliamentary system outlined by a constitution and supported by a largely ceremonial

president. The official title of the government is Governo TimorLeste.

Figure 32: Map of TimorLeste. Source: (U.S. Central Intelligence Agency 2003)

The Portuguese were the first Europeans to arrive in the area in the 16th century and they

established an isolated presence on the island of Timor. The Portuguese ruled TimorLeste for

more than 400 years. Portuguese Timor declared itself independent on November 28, 1975,

but was invaded and occupied by Indonesian forces 9 days later (Budiardjo and Liong 1984).

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The territory was subsequently declared the 27th province of Indonesia in July 1976 as Timor

Timur (Hiorth 1985). Internationally, its legal status was that of a "nonself governing

territory under Portuguese administration".

During the following guerilla war an estimated 100,000 to 250,000 persons lost their lives. On

August 30, 1999, in a United Nations (UN)supervised popular referendum, the East

Timorese voted for full independence from Indonesia. Tragically, violent clashes instigated

primarily by antiindependence militias broke out soon afterwards. UN peacekeepers led by

Australia were brought in to restore order. Independence was internationally recognised on

May 20, 2002 and East Timor joined the UN on September 27 of that year (Governo Timor

Leste 2002).

After the independence poll in 1999 most of TimorLeste’s infrastructure was destroyed.

International aid is currently assisting Timorese to rebuild the country’s economy. Industries

are only beginning to develop, so most manufactured items are imported, increasing the cost

of living for many. Major industries revolve around the production of coffee (the major

export), rice, maize, logging, fisheries, spices, coconuts and cacao (Hiorth 1985). Tourism is

considered a potential source of development, however, hotels, transport and other facilities

necessary for tourist ventures are still lacking. Oil and natural gas exploration will become a

major source of income for TimorLeste as the Timor Gap fields are developed.

Regionally distinct groups reside in TimorLeste including people of MalayoPolynesian and

Papuan background. A small Chinese minority is also present. Each of Timor’s 13 districts is

culturally and linguistically unique (Hiorth 1985). Stories, singing, music and dancing play an

integral part in people’s lives. The majority of the population live in small villages and grow

their own food through subsistence agriculture. Rice and corn are the main staples, while

chicken, sheep and pigs are also farmed for food. Access to running water and electricity is

much less available in rural areas, in comparison to cities such as Dili or Baucau. Around

42% of the population live below the poverty line (U.S. Central Intelligence Agency 2004).

TimorLeste is the poorest nation in southeast Asia.

The Catholic Church has been a dominant institution in Timor since the arrival of the

Portuguese. Catholics are the major religious group in TimorLeste (greater than 90%).

Muslims, Hindus and Buddhists, are also present (U.S. Central Intelligence Agency 2004).

3 Methodology


Many people also maintain animistic beliefs connecting them to the spirits of the dead,

through stones, animals, wells or streams.

3.3 Field Trip Site

Field work was conducted from the 21st June to the 14th July 2004 in the southern half of

TimorLeste in the Samé region. (also known as the Manufahi region (Bouma and Kobryn

2002)). This area is highlighted in yellow on Figure 33. The distance between Samé and Dili

is approximately 50km, and between Samé and the south coast is approximately 20km. The

base camp for the project was located at a house in the town of Samé, situated on the southern

side of the Kablaki range. To reach Samé it was necessary to drive using vehicles that were

capable of traversing muddy and broken terrain and tracks. This was due to the poor state of

the road linking Samé and Dili, which was often muddied or nonexistent due to past

landslides and rainfall in the mountainous area.

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Figure 33: Map showing sampling region in TimorLeste. Source: (U.S. Central Intelligence Agency2003)

3.3.1 Living Conditions

In Samé, both Timorese and Australia students and staff lived and worked in a house rented

from the local District Administrator. Three vehicles were shared amongst the group as daily

field work was conducted in the surrounding area. An intermittent power supply was available

every few nights. During periods without power a generator was usually available for use

when necessary. Running water, a septic tank system and refrigeration was also accessible. A

makeshift “laboratory” including a sheltered workspace and bench was constructed from local

bamboo and tarpaulins.

3 Methodology


Figure 34: A photo of the house rented in Saméduring the field trip. The makeshift laboratory islocated to the left of the house. Source: AlexCullen 2004.

Figure 35: A photo of the water facilitiesavailable at the house in Samé. Source: AlexCullen 2004.

3.3.2 Weather Conditions

As mentioned in Chapter 3, section 3.2, the climate of the study site is tropical with two

distinct seasons: wet and dry. The field operation for the project began near the start of the dry

season. Major rains ended approximately two weeks after arrival in TimorLeste. Water levels

in rivers had considerably decreased in this time, as well as during the period of our field

work. Light rains occurred intermittently during the field operation. Temperatures were

approximately 25°C during the day and cooled significantly during the night. This contrasted

to weather in Dili which was generally warmer and more humid during this time, and less

cool during the night.

3.3.3 Land Use in the Study Site

From observation, tropical and subtropical forests were present in the region, especially in and

around the mountainous areas. Deforestation was observed in the form of fuel wood

collection, livestock grazing and clearing for agricultural use, particularly near the flatter,

coastal areas. Coffee plants were often seen growing beneath larger trees in forested areas,

and were handpicked by locals in nearby villagers. No machinery was observed for the

maintenance of the coffee plantations during the operation. Closer to the coast, areas cleared

for farming was more familiar. Rice and corn crops were the most common agricultural

activities. However, individual crop fields were relatively small, usually belonging to one or

more families within a community, and no intensive agricultural areas were apparent. A

variety of farm animals were observed throughout the region. and were not usually confined

3 Methodology


to any one area. Animals included cows, buffalo, dogs, horses, donkeys, goats, pigs and

chickens and did not appear to be confined to any one area. It was common to notice such

animals and animal tracks around rivers and other water bodies.

Figure 36: A photo of a local corn crop,Samé, TimorLeste. Source: Alex Cullen2004.

Figure 37: A photo of a traditional Timorese house,Samé, TimorLeste. Source: Alex Cullen 2004.

In the major towns, local housing was constructed of brick and cement, although often in need

of repair from the recent civil instabilities. The lack of resources also contributed to the

degradation of the existing housing. Outside of the larger towns, local housing was

constructed of traditional thatch, bamboo and wood. Vehicles were relatively uncommon in

this region and the majority were used by the local administrators, police and foreigners.

3.4 Goals and Limitations

A major goal of the project was to design a methodology that would provide useful

information on water quality using simple, inexpensive techniques and equipment. The

limitation of working in a region where infrastructure is lacking requires such a practical

design. Devising a cheap, simple and robust process has applications for developing countries

in general. It may provide an applicable method for monitoring water quality in locations

where basic utilities and resources may not be accessible.

Techniques were thus chosen where both sampling and analyses were not limited by lack of

electricity or relatively expensive equipment. Simple methods were also chosen to minimise

the potential for human error in cases where extensive training in field work and laboratory

analyses were not possible. All sampling and analyses were replicated to determine the

variability in results due to error.

3 Methodology


3.5 Logistics of Analysing Water Quality in a Developing Nation

The lack of facilities available in a developing nation may often limit the application of

research techniques usually conducted in more prosperous countries. Therefore, to

successfully perform research in developing regions, it is necessary to create simple initiatives

to either replace or to redesign these techniques. By doing so, simple data may still be

collected and patterns may become evident if such modified techniques are repeated in a

monitoring program. It also provides a cheaper and simpler alternative for repetitive analyses.

From a political and social perspective, conducting scientific research in a developing nation

may also be more effective by involving as many members from society as possible, from as

many levels as possible. Such interest was especially enhanced in TimorLeste by arranging

exchanges of knowledge and training at the government level, and by contributing basic

resources (such as stationery to schools) and being involved with communities (such as

participating in games and soccer matches) at a local level. Support from the government of

TimorLeste was provided to those involved in this project by lending vehicles, providing any

maps or scientific and social information on the given subject matter, and by encouraging

staff from the Department of Energy and Mineral Resources and Department of

Environmental Services to contribute their assistance to the field operation. Consultation with

local Timorese from villages within the study site resulted in the acquisition of information

regarding the possibility of accessing various sections of rivers, as well as the types of land

uses and histories of the area.

3.5.1 Taking Initiatives

After considering the facilities available in Samé it was necessary to modify some of the

proposed analytical techniques. The particular limitations and the initiatives to counteract

these are listed in below in Table 31.

Table 31: Methodological limitation and initiatives taken to counteract these during the field operation inTimorLeste.

Methodological Limitation Initiative Taken

• Lack of ice for refrigeration of samplesafter initial collection, for prevention ofchlorophyll a degradation.

• Samples wrapped in aluminum foil orblack plastic, placed in sky and kept outof direct sunlight to minimisedegradation.

3 Methodology


• Lack of power for refrigeration of watersamples, for prevention of chlorophyll aand chemical degradation.

• Filtration and analysis of chlorophyll aand chemical concentrationsimmediately after return to base camp,to minimise possible degradation. Ifnecessary, storage in the darkest, coolestlocation possible.

• Lack of Agene bottles to collect watersamples.

• Plastic water bottles bought locally wererecycled for sampling.

• No instrumentation to measure riverflow (e.g. Flow Meter)

• Measure volume flow using a timer,similarly sized buoyant objects(velocity), and tape measure (riverwidth and depth).

• No facilities for safe disposal ofhazardous waste for high accuracychemical analyses.

• Use simple semiquantitative analysesthat did not require hazardouschemicals. Waste disposed of with highvolume dilutions into septic tanksystem.

• Breakdown of the YEOCAL probe (pH,temperature, salinity).

• Measure pH using litmus paper,temperature using a thermometer andsalinity using a conductivity meter.

• Lack of bench space/tables for basicanalyses.

• Construction of a makeshift bench usinglocal employees and knowledge fromtarpaulins, rope and bamboo.

• Lack of detail in physical and digitalmaps to determine accessible locationsfor sampling.

• Consultation of local villagers as to thelocation of roads/tracks for access tovarious sections of rivers.

By developing such initiatives it was possible to develop the most efficient strategy for

sampling and analysis in the time available. From this study a series of protocols may be

developed to create a Water Quality Manual for developing regions. The specific initiatives

for each technique are discussed in more detail in the sections below.

3.5.2 Accessing Materials

One of the considerations for choosing the particular methodologies employed in TimorLeste

was the possibility of accessing materials that were necessary to conduct the methods. The

chemicals and equipment necessary for the measurement of each of the biological, chemical

and physical parameters were either replaceable with available similar appropriate materials,

or relatively easily imported. This is discussed for each individual technique later in this

chapter. A list of all the chemicals and equipment used during the field operation is available

in Appendix F.

Such techniques were chosen so that they could be repeated by the Timorese themselves,

instead of the alternative of collecting samples to be sent overseas for analysis. This allows

3 Methodology


more use for the results of this study, as well as contributing to the development and

ownership of such research by the people of TimorLeste. In this way, the better management

of the natural resources of the country can occur by the Timorese themselves.

3.6 Sampling Design

A sampling regime was determined upon the acquisition of a 1:25000 topographic map of the

Samé region and southern coastline (Dibuat Dan Diterbitkan Olem 1993b; Dibuat Dan

Diterbitkan Olem 1993a). A large (regional) and small (local) scale sampling set were both

conducted. The small scale sampling set was incorporated as a subset of the larger scale

sampling design consisting of 24 sampling locations (R1R24) in total. These are presented in

Figure 38. The sampling area extended from the Kablaki mountain range, north of Same

(outlined in red in Figure 38), to the south coastal town of Betano (outlined in yellow in

Figure 38.). Samples were also taken from a major river mouth located on the western side of

Betano, also shown in Figure 38. The exact latitudinal and longitudinal positions of each

sample site are listed in Appendix A.

3 Methodology


Figure 38: Topographical map of south coast of TimorLeste showing sampling locations R1R24.Source: (Dibuat Dan Diterbitkan Olem 1993b; Dibuat Dan Diterbitkan Olem 1993a)

The aim of the large scale sampling set was to determine if regional variation existed for any

of the biological, chemical and physical parameters. An attempt could then also be made to

explain the presence of any variation in the mountaincoast gradient based on the possible

3 Methodology


interaction of these parameters and any downstream cumulative effects, in particular,

examining the possible delivery of riverine pollutants or nutrients to the coast.

The small scale sampling regime involved sampling at four locations (R9R12) from a

concrete drain running along the side of the road through the town centre of Samé. The

objective for this sampling regime was to determine whether water quality significantly

altered between the top and bottom end of the town. If any change was noted it would

therefore be possible to make inferences about the compound effects of a human population

living in direct proximity to a water source.

Figure 39: Taking measurements from drains running down the side of the road in the town of Samé,TimorLeste. Source: Dr Grey Coupland 2004.

3.6.1 Field Sampling

Sampling locations were chosen according to ease of access. In particular, whether rivers

were in close proximity to roads or tracks or within easy walking distance. Such decisions

were made from both digital and physical maps or from consultation with locals familiar with

the area. These decisions were necessary because of the amount of equipment that needed to

be carried from the vehicle to the sampling site. A Global Positioning System (GPS) was used

to record the exact latitudinal and longitudinal coordinates of each location for later

3 Methodology


identification on digital maps. Detailed notes were also taken at each location documenting

the following (see Appendix E):

• People involved with sampling

• Date and time of day

• Weather conditions

• Type and abundance of vegetation

• Human and animal activity

• Geology, flow, biology and colour of water source

These observations were recorded in order to provide the opportunity to relate results to the

conditions and environment in which they were collected.

All physical parameters were measured and recorded in situ using a YEOCAL probe.

Chemical and biological measurements were recorded from analyses later conducted on

samples obtained from each site. All of the field work conducted during the length of the trip

was done with the assistance of staff from the Department of Energy and Mineral Resources

and students from Dili Polytech University in TimorLeste.

Replicate samples were collected in 500ml plastic bottles from each location. Vessels were

thoroughly rinsed with water from the surface layer of the river or equivalent water source

before collection. This procedure was exercised to lower the risk of sample contamination.

Water was collected upstream of where the person carrying out the collection stood. In this

way, any mixing in the water column from the movement of the sampler could be avoided.

The filled bottles were either wrapped in aluminium foil or black plastic bags and placed in an

esky kept out of direct sunlight. The purpose of this step was to minimise degradation of

chlorophyll a and any nutrients and metals present in the sample. Samples were transferred to

a refrigerator for storage until further analyses were conducted.

3.6.2 Water Quality Parameters

The parameters chosen for analysis of water quality for this study are presented in Table 32

below.

3 Methodology


Table 32: Parameters chosen for assessing water quality in TimorLeste.

Parameter Type ParameterPHYSICAL • Temperature

• Salinity• pH

CHEMICAL • Nutrients:o NO3o NO2o NH+4o PO43

• Metals:o Cuo Feo Ag

BIOLOGICAL • Chlorophyll a

These parameters were primarily chosen from an assessment of the standard characteristics

for measuring water quality presented from literature (as shown in Chapter 3) and from the

recommendation of experts (Dr Anya Waite, pers. comm., 13/05/2004). By measuring these

parameters it also provided the possibility of determining both their individual and collective

effects on riverine water quality. Another significant reason for choosing these parameters is

the practicality of their measurement in a developing region. The difficulties associated with

freighting chemicals and transporting equipment by plane was a major constraint to the type

of analyses that could be conducted. The limited facilities in TimorLeste also had to be

considered in choosing the methods, and thus parameters that could be employed. This is

discussed in further detail in Chapter 5.

3.7 Physical Parameters

3.7.1 Temperature, Salinity and pH

Temperature, salinity and pH were measured in situ using a YEOCAL probe. The YEOCAL

was calibrated from these parameters in Australia approximately one week before the

beginning of the field trip. At each sample site, the instrument was lowered into surface

waters at a central position within each water source, in order to prevent a potentially biased

result that might have arisen from near shore measurements. This technique is demonstrated

in Figure 310 below. Measurements were recorded three times and a mean value calculated

from these. Temperature was recorded in degrees Celsius (°C) and salinity in parts per

3 Methodology


thousand (ppt). Salinity levels were also converted to electrical conductivity (EC) units

S/cm) and standardised to 25°C by increasing the value by 2% per degree for locations

where water temperatures were below 25°C and decreasing them by 2% per degree where

temperatures were above 25°C (Waterwatch Australia Steering Committee 2002). pH

evaluations were carried out using litmus paper at several locations and conductivity

evaluations using batteryoperated Conductivity Meters. The purpose of these measurements

were to test their usability, against the YEOCAL probe as backup options. This method was

employed to test the reliability of the technique in replacement of probe measurements. The

raw data for these measurements is presented in Appendix B.

Figure 310: Gaspar da Costa da Jesus (ERM)taking in situ phyiscal measurements using theYEOCAL probe. Source: Dr Grey Coupland2004.

Figure 311: Taking a hand measurement of riverwidth to calculate Volume Flow rate. Source: DrGrey Coupland 2004.

Dissolved oxygen and turbidity were not recorded due to the failure of the probes measuring

these parameters in the YEOCAL. Notes were instead taken at each location regarding the

visibility of the water as clear, partially turbid or completely turbid.

3.7.2 Flow Rate

Flow rates were determined at each location by multiplying measurements of width, average

depth and average velocity as shown in Equation 31 below.

Equation 31: Determination of Volume Flow Rate.

vDWF **=

3 Methodology


Where: F = Volume Flow Rate (m3s1)

W = Width (m)

D = Average Depth (m)

v = Average Velocity (ms1)

River widths were calculated by hand using measuring tape as demonstrated in Figure 311.

Average depths were estimated by calculating the mean of a series of hand measurements

using tape. To measure average river velocity, the time taken for three similarly sized buoyant

objects (locally available leaf litter and debris) to travel a set distance was recorded. A mean

velocity was calculated by dividing the set distance with average time of travel.

Ideally, measurements of river flow would also be taken using more sophisticated

instrumentation such as a Flow Meter. Such a device might a provide a more consistent and

accurate measurement. However, under the circumstances this technique gave a general

indication of the type of flows occurring at each sample site. A comparison of the simple hand

technique to an electronic in situ measurement would provide valuable information as to the

accuracy and thus effectiveness of using such a basic hand measurement.

3.8 Chemical Parameters

The original methodology intended for the measurement of nutrient and metal concentrations

involved high resolution analysis as described by Parsons et al. (1984).

Techniques for the determination of nutrients such as nitrate and phosphate provide

concentrations at a precision in the range of 3 20 g/L level (Parsons et al. 1984). The

equipment required to perform these analyses included sensitive glassware that was not

readily available in a plastic form (for ease of transport), at the time near departure for the

field operation. Hazardous chemicals were also necessary to conduct the analyses. For

example, a Nitrate analysis would require the following chemicals (Parsons et al. 1984):

• Zinc Sulfate (ZnSO4)

• 6M NaOH

• Sulfanilamide

• 1napthyl ethelenediamine dihydrochloride

• 1M HCl (SP GR 1.18)

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• Granulated CopperCadmium

• Copper Sulfate (CuSO4)

• Ammonium Chloride (NH4Cl)

• Copper Wool

• Potassium Nitrate (KNO3)

To transport these chemicals would be extremely difficult due to varying degree of toxicity of

these chemicals and their various “Dangerous Goods” classification, as defined by the

Australian Customs Service and Department of Defence (2004). Those chemicals that could

be transported might not be done so in the same airplane as another, and only in certain

restricted quantities in certain packaging. If any of these factors are considered doubtful by a

custom’s officer then the chemicals may be legally detained. To organise the export of such

substances also requires complex, expensive documentation. A pilot flying an airplane

organised to carry such substance also has the right to refuse their placement on the craft at

any time. Thus the certainty of receiving such chemicals requires extensive planning and

money, and may still be in doubt.

Another issue that was considered was the availability of facilities that could allow the safe

disposal of such chemical substances. Also, any substances that could not be disposed of in

TimorLeste would require similar documentation, packaging and planning if they were to be

returned to Australia for disposal.

The extreme difficulty with organising the transport and use of such chemicals and equipment

resulted in a complete reworking of analyses for measuring nutrient and metal concentrations.

The alternative option considered was the application of semiquantitative test strips, to

measure various nutrient and metal concentrations. These portable water quality test strips are

recommended by United Nations Environment Programme and World Health Organisation

(1996) as a useful method for the field testing of samples in a water quality monitoring

program. Most portable kits can be easily transported without notifying customs, are small

and simple to use and can be easily disposed of.

Test strips were bought from the companies: Aquaspex and Enviro Equip. Strips were used to

measure the following dissolved chemicals in the sample replicates from each of the 24

sample sites:

3 Methodology


• Nutrients:

o Nitrate (NO3)

o Nitrite (NO2)

o Ammonium (NH+4)

o Phosphate

• Metals:

o Iron (Fe)

o Copper (Cu)

o Silver (Ag)

Figure 312: Ammonium and Phosphate TestStrips. Source: Alex Wyatt 2004.

Figure 313: Using test strips for semiquantitative analyses of nutrients and metals.Source: Alex Wyatt 2004.

3.8.1 Nutrients

Colorimetric test strip kits included a number of paper strips coated with a reactant at one end.

A colour chart was also included in each kit and for some of the analyses, extra reagents. Such

kits for measuring PO43 and NH+4 are illustrated in Figure 312 above.

Sterile gloves and glasses were worn during these analyses to prevent contamination of the

samples and contact with any of the reagents. Samples were tested as soon as possible after

collection and under similar light conditions, in the same location each time (as shown in

Figure 313). Test strips were placed (with the reactant end down) and moved around in a

certain amount of each sample, for a specified length of time as indicated on each kit. If

necessary, extra reagents of a certain volume were also added. After this procedure was

completed, the strip was removed from the sample and shaken slightly to remove excess

moisture. Each strip was placed onto the same white sheet of paper to more easily identify any

3 Methodology


contrasts in colour that appeared at the end of each strip. The colour at the end of the strip was

compared to those of a provided colour chart. Each colour on the chart represented a different

dissolved concentration of the specific nutrient that was being tested for. The colour of the

strip considered most similar to that on the chart was noted and thus assigned that particular

concentration.

Due to the subjective nature of colour assessments, at least two people were involved in

identifying the match of test strip colours to those of the chart. Detailed results of these

analyses are provided in Appendix C.

3.8.2 Metals

A similar procedure was conducted with test strips to measure the concentrations of Iron (Fe),

Copper (Cu) and Silver (Ag) as described in section 3.8.1. These metals were primarily

chosen as a general representation of metals in water, due to their universal presence in the

environment. They were also chosen because of the availability of the test strips required to

measure these particular metals (in particular, silver was cheaply available).

3.9 Biological Parameters

3.9.1 Chlorophyll a

Chlorophyll a biomass was calculated from replicate samples taken at each of the 24 locations

shown in Figure 38. Samples were wrapped in aluminium foil or black plastic, placed in an

esky and kept out of direct sunlight as much as possible after their initial collection. Samples

were filtered as soon as possible after collection. This was due to the lack of ice for immediate

storage and intermittent power supply required for refrigeration. Both these strategies were

designed to minimise the degradation of the chlorophyll a in the samples from exposure to

heat and light.

A variation on the techniques of extraction by sonication and fluorescence described by

Welschmeyer (1994) and Parsons et al. (1984), were conducted on the samples. Glasses and

sterile gloves were worn by all those involved in the analyses to prevent contamination of the

samples. The procedure for chlorophyll a analysis conducted during the field operation is

outlined below:

3 Methodology


Volumes of approximately 200ml to 500ml from each of the 48 samples were filtered one at a

time through 0.45 m Whatman GF/F filters. Filtration was conducted in a room in almost

complete darkness to prevent degradation by light, using a hand pump in conjunction with a

plastic filter manifold. The plastic manifold and pump was rinsed each time with the water of

the sample that was to be filtered, to minimise contamination. Filtration was undertaken as

soon as possible on the same day as the collection of the samples to prevent degradation of the

chlorophyll.

After filtration, filter papers were handled and folded with samplerinsed tweezers and placed

into 13mm sterile labelled test tubes. Each test tube was then filled with 8ml of 90% ethanol

using a 10ml plastic syringe. 90% Ethanol was applied as the extraction solvent. 90% Ethanol

was chosen as it is the standard solvent for chlorophyll a extraction, as outlined in the ISO:

10260, 1992 standard (Papista et al. 2002). Both ethanol and hydrochloric acid required for

fluorescence measurements had to be freighted to Dili by ship due to its “Dangerous Goods”

classification.

Glass tubes were covered with a piece of parafilm “M” laboratory film to prevent evaporation.

Test tubes were placed in a test tube rack and covered with aluminium foil and placed in

refrigeration for 24 hours. After 12 hours of this period each test tube was thoroughly shaken

by hand for 1 minute and returned to the rack and refrigerator.

Following 24 hours of extraction, samples were removed from refrigeration to return to an

ambient temperature. The TD700 fluorometer transported from Perth was given time to warm

up and later calibrated using 90% ethanol. The test tube rack was placed next to the

fluorometer during the analyses and kept covered by aluminium foil. The parafilm was

removed from the test tubes containing both the 90% ethanol solvent and filter paper and were

analysed one at a time. Tweezers rinsed with 90% ethanol (to prevent contamination between

samples) were used to remove the filter paper from each test tube. Each tube was then placed

into the calibrated fluorometer and its concentration recorded as Rb (before acid result) after

the same value remained on the screen of the fluorometer for at least 30 seconds. 3 drops of

1mol Hydrochloric Acid (HCl) were added to the tube. Fresh parafilm was replaced over the

tube and the tube was shaken by hand and left to settle for 60 seconds. The test tube was then

placed in the fluorometer for the second time and the reading recorded as the Ra (after acid

result). This method was also applied to a blank sample of an unused filter paper placed in

9ml of 90% ethanol.

3 Methodology


After analyses were completed glass test tubes were disposed of in the Samé rubbish system.

Ethanol and acid wastes were collected from each sample. These chemical wastes were

disposed of by pouring them into the house septic tank system and diluted with large volumes

of water.

The chlorophyll a concentrations of each sample was calculated using the formula presented

in Equation 32 .

Equation 32: Calculation of chlorophyll a (Centre for Water Research 2003).

( )VvRR

rrLgalChlorophyl ab **

1)/( −

−=⋅⋅ µ

Where: r = beforetoafter acidification ratio of a pure chlorophyll a solution = 2.2

Rb = fluorescence of a sample prior to acidification

Ra = fluorescence of a sample after acidification

v = volume of the solvent extract (mL)

V = volume of the original filtered sample (mL)

Calculations of chlorophyll a concentrations from raw data are presented in Appendix D.

3.10 Source of Errors

3.10.1 Sampling Error

Possible error in the results of water quality data may have been magnified by the following

processes during sampling:

• Contamination of reused bottles from inadequate washing at each sample site.

• Error from degradation of the sample, due to inadequate protection from sun and heat

during sampling.

• Error from the inadequate representation of water samples at each site. Only one point

in the river was sampled at each site. Replicates were taken at each point, however

bias may have been introduced as a result. Ideally samples from different points at

each site would have been taken. This was not possible due to equipment limitations.

3 Methodology


3.10.2 Error in Physical Measurements

Possible error in the results of physical data may have been magnified by the following

processes during analysis:

• Inaccuracy of hand measured volume flow rates. Procedural inaccuracies may

have resulted from: Human error from timing the velocity of buoyant objects, from

inaccurate width and depth measurements (from handheld tape measurements)

and from an inadequate number of measurements of depth and velocity when

calculating an average for these.

• Error from the inadequate representation of in situ characteristics at each site.

Three YEOCAL measurements at a central location at sample site may not have

been enough to adequately represent the physical conditions there.

3.10.3 Error in Chemical Measurements

Error in test strip analyses may have occurred from the following:

• Degradation of any nutrients or metals present due to the lack of refrigeration for

the samples or exposure to light.

• Inadequate contact of the test strip to the samples or reagents.

• Poor lighting resulting in misjudged colour matches to the provided colour chart.

3.10.4 Error in Biological Measurements

The chlorophyll a analysis performed in TimorLeste differed from the standard described by

Parsons et al. (1984), Welschmeyer (1994) and Arar & Collins (1997). A modified analysis

was conducted because of a lack of available facilities. Some of these alterations may have

contributed to error in the measurement of chlorophyll a. These include:

• Initially storing collected samples in ambient temperatures, due to the lack of ice

for transporting samples. This may have contributed to the degradation of

chlorophyll a in the samples, because of exposure to heat (Arar and Collins 1997).

3 Methodology


• Storing collected in samples in cool but not freezing temperatures, due to the lack

of a freezer. This may have contributed to the degradation of chlorophyll a in the

samples, because of exposure to heat (Arar and Collins 1997).

• Only shaking test tubes containing the ethanol extract and filter paper once by

hand, instead of using an ultrasonic bath and shaking more frequently. This may

have resulted in less chlorophyll being extracted from the filter paper (Arar and

Collins 1997).

• Removing filter paper from test tubes and measuring chlorophyll levels in the

same tube, instead of filtering into a new sterilized tube. This may have distorted

results of the fluorescence because of the presence of dissolved fragments of filter

paper.

Samples from sites R17R24 (see Figure 38) had to be analysed for chlorophyll a in the room

of a hotel in Dili, due to time constraints of the field operation. As a result, the test tubes

containing the filter paper and 8ml of ethanol solvent for these replicate samples were

transported by bus without ice or refrigeration for approximately 5 hours. The rack of test

tubes were kept covered and in the coolest position possible during the bus trip. It is likely

that some degradation of chlorophyll occurred in the test tubes under such conditions

Potential sources of error associated with chlorophyll a analyses are also described by Arar

and Collins (1997) and include:

• Interference in the measurement of chlorophyll a due to the possible presence of a

substance that fluoresces in the red region of the spectrum.

• Underestimation of chlorophyll a due to the possible presence of chlorophylls b and c.

• Possible degradation of chlorophyll a concentrations due to exposure of light during

analysis.

• Possible contamination of filters due to contamination of tweezers, pasteur pipette or

manifold filter by another sample.

Papista et al. (2002) also mention the potential error related to the use of ethanol as a solvent.

The molecular interactions of ethanol with both water and chlorophyll can result in

misleading results. Although Papista et al. (2002) states that methanol provides more accurate

results than ethanol, it is however, deemed a better solvent than acetone.

4 Results


4 RESULTS

The results presented in this chapter are some of the first water quality data for TimorLeste.

In the sections below, the basic physical (temperature, pH and salinity), chemical (nutrient

and metal concentrations) and biological (chlorophyll a concentrations) data for the Samé

river system are presented. Regional and local scale biological data sets are also displayed, in

order to illustrate any patterns that are evident at these levels. All values are shown with

standard deviation where applicable.

4.1 Physical Data

The physical results were recorded at the surface of the river at each of the 24 sites. They

represent the surface conditions of each water body. These results are summarised in Table

41 below. Results from two other river systems in the regions (the C and the VKD of

Indonesia) are also presented for comparative purposes. The measurements taken in

Indonesian rivers were collected in the same season (the dry season) as the measurements in

TimorLeste (Palupi et al. 1995). Only dry season data is presented to remove any uncertainty

regarding variation in data that might be attributed to climatic conditions.

Table 41: Temperature, pH, salinity and flow rates of the Samé river system.

Maximum Minimum Average Standard Deviation

Temperature (°C) 27.9 18.8 23.1 2.86

pH 7.46 6.27 6.96 0.26

Salinity (ppt) 0.44 0.39 0.42 0.02

Flow Rate (m3s1) 14.00 0.22 2.99 3.88

4.1.1 Temperature

Considerable variation in temperature was observed along the Samé river system, compared

to similar systems in the region. The temperature of the Samé River was very similar and

slightly lower than two other Indonesian river systems – the Ciliwung and the Sunter. The

variation in temperature along these rivers was much lower than in the Samé system (Figure

31). This may be explained by a difference in the number of sample sites: only five positions

were measured in each of these systems, whereas 24 positions were sampled in the Same.

4 Results


Average Temperature in Timorese & Indonesian Rivers

0

5

10

15

20

25

30

35

Indonesian & Timorese Rivers

Tem

pera

ture

(deg

C)

Same

Ciliwung

Sunter

Figure 41: Average temperature in Same, Ciliwung and Sunter rivers. Data for the Ciliwung and Suntersystems from Palupi et al. (1995).

4.1.2 pH

The pH conditions of the surface water of the Samé river system were generally homogeneous

and close to neutral (Table 41). At each site the Litmus paper method gave a consistent result

of pH 7, similar to the YEOCAL average value. The average pH levels of the Ciliwung and

Sunter rivers (7.31 +0.18 and 7.42 +0.25, respectively) were also close to neutral yet slightly

higher than that of the Samé river system (Palupi et al. 1995) (Figure 42).

4 Results


Average pH of Rivers in Indonesia & TimorLeste

2

4

6

8

10

12

14

Timorese & Indonesian Rivers

pH

Same

Ciliwung

Sunter

Figure 42: Average pH of the Same, Ciliwung and Sunter river systems. The Ciliwung and Sunter riversare located in Jakarta, Indonesia. Data for these systems is sourced from Palupi et al. (1995).

4.1.3 Salinity

The salt content of the Samé river system did not vary significantly across the region. The

range in salt concentration was relatively low with only 0.05 parts per thousand (ppt) between

the maximum and minimum levels (Table 41). From this observation it may be assumed that

there are no significant salt inputs along the length of the river and or from the connection to

the ocean. The salt concentration data was also converted to electrical conductivity (EC) and

standardised to a temperature of 25°C in order to compare the results to those of Hart et al.

(2002) and Sulistyawan & Hartono (2002) for the Saguling and Maro rivers in Indonesia. The

EC values of the Maro and Saguling systems were 1562.5 S/cm and 225.6 + 103.8 S/cm at

25°C, respectively. The average EC in the Same system was 680.1 + 53.4 S/cm at 25°C

less than half that of the Maro river and approximately three times that of the Saguling

reservoir near Jakarta, Indonesia (Hart et al. 2002) (Figure 43).

4 Results


Average Electrical Conductivity of Rivers in Indonesia & TimorLeste at aStandardised Temperature of 25 degrees C

0

200

400

600

800

1000

1200

1400

1600

1800

Timorese & Indonesian Rivers

EC (m

icro

S/cm

)

Same

Saguling

Maro

Figure 43: Average electrical condutivity of the Samé, Saguling and Maro river systems, standardised to25°C. Data for the Saguling system sourced from Hart et al. (2002) and for the Maro system sourced from

Sulistyawan & Hartono (2002).

4.1.4 Flow Rates

Volumetric flow rates varied considerably across the 24 sample sites, from a maximum of 14

m3s1 to a minimum rate of 0.22 m3s1 (Table 41). The standard deviation for these

measurements was greater than the value of the average flow, indicating high variability.

Flow rates were also very similar to those of the Ciliwung and Sunter rivers: ranging from 0.1

– 14.5 m3s1 in the dry season (Palupi et al. 1995).

4.2 Chemical Data

Nutrient and metal concentrations were determined using semiquantitative test strip analyses.

The results of the following nutrients and metals that were tested for is presented in Table 42:

• Nutrients:

o Nitrate (NO3)

o Nitrite (NO2)

o Ammonium (NH+4)

o Phosphate (PO43)

• Metals:

o Iron (Fe)

4 Results


o Copper (Cu)

o Silver (Ag)

Table 42: Average nutrient and metal concentrations of the Same, Ciliwung, Sunter and Saguling riversystems in the dry season. Data for the Ciliwung and Sunter systems is sourced from Palupi et al. (1995)

and for the Saguling system is sourced from Hart et al. (2002).

Samé Ciliwung Sunter Saguling

NO3 (mg/L) 0.5 1.39 0.81 1.35

NO2 (mg/L) < 0.15 0.25

NH+4 (mg/L) < 3 0.39

PO43 (mg/L) < 3 0.48 0.57 0.1

Cu (mg/L) < 0.5 0.022 0.015

Fe (mg/L) 0.02

Ag (µg/L) 50

Due to the semiquantitative nature of the nutrient and metal analyses, results could only be

determined as a range in concentration. None of the samples contained concentrations of

nutrients and metals greater than the lowest possible range available in each test strip analysis

(Table 42).

In comparison to the Ciliwung, Sunter and Saguling rivers in Indonesia, NO3 concentrations

in the Samé system were lower than those in all three systems, and at least half of that in the

Ciliwung and Saguling systems. NO2 levels were also greater in the Saguling river, almost

double that of the Samé river. NH+4 and PO43 concentrations were found to be less than

3mg/L in the Samé river system, however, it was not possible to determine whether

concentrations were greater than those in the Indonesian rivers due to the lack of resolution in

the nutrient analyses. The same was true when comparing Cu concentrations in the Samé river

system to those in the Ciliwung and Sunter rivers, where Cu levels were found to be

0.022mg/L and 0.015mg/L respectively. Fe and Ag concentrations were not available for

comparison in the three Indonesian rivers.

4 Results


4.3 Biological Data

Algal biomass was represented by measurements of chlorophyll a concentration. Chlorophyll

a concentrations were compared to distance downstream on both a local and regional scale to

investigate any relationship between these variables.

4.3.1 Chlorophyll a

Chlorophyll a concentrations varied by two orders of magnitude in the 48 samples taken from

the Samé river system. The highest and lowest concentrations measured were 2.04 g/L and

0.015 g/L, respectively. The average value of chlorophyll a was found to be 0.47 + 0.46

g/L. Chlorophyll a was also measured in the local tap water to test for any differences from

the stream water. The average chlorophyll a concentration for tap water was found to be very

similar to that of river water at 0.50 + 0.34 g/L.

Regional Scale

To determine whether a relationship existed between chlorophyll a concentration and the

distance downstream in the Samé river system, a linear regression was performed using the

least squares method. This was carried out to test whether chlorophyll a increased in

concentration downstream, due to accumulation affects of nutrients. The longitudinal distance

from each sample site to the river mouth was used as a proxy for distance downstream. No

statistically significant linear trend was determined at the regional scale as indicated by the

low R2 value (Figure 44).

4 Results


Chlorophyll a concentration vs Longitudinal Distance from the Sample Site to theRiver Mouth in the Same River System

R2 = 0.0234

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

Logitudinal Distance (km)

Chl

orop

hyll

a (

g/L)

Figure 44: Chlorophyll a concentration versus longitudinal distance from each sample site (R1R24) inthe Samé river system.

A linear regression was also employed to test whether algal biomass was influenced by the

flow rate of the river. No statistically significant correlation was found to occur between

chlorophyll a concentrations and river flow rate (Figure 45).

Chlorophyll a Concentration vs River Flow Rate

R2 = 0.0021

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 2 4 6 8 10 12 14 16

Volume Flow Rate (m^3/s)

Chl

orop

hyll

a (m

icro

gram

s/L)

Figure 45: Chlorophyll a concentration versus river flow rate in the Samé system.

4 Results


Local Scale

Despite the few sample sites, an obvious trend was observed in chlorophyll a concentration as

the drain water flowed through the town (Figure 46). Chlorophyll a increased by an order of

magnitude (from 0.06 + 0.03 g/L to 0.45 + 0.08 g/L) from where the stream entered to where

it exited the Samé drainage system. A statistically significant positive linear relationship was

determined between chlorophyll a and distance downstream at a local scale (p<0.05, n = 4).

The significance was calculated using Pearson’s correlation coefficient and a 2tailed ttest,

assuming normal distribution (University of the West of England 2001).

Distance from First Sample Location in Town Drain vs Chlorophyll a

R2 = 0.8517p < 0.05

0

0.1

0.2

0.3

0.4

0.5

0.6

0.5 0 0.5 1 1.5 2 2.5 3 3.5

Distance Downstream from First Sample Location (km)

Chl

orop

hyll

a (

g/L)

Figure 46: Chlorophyll a concentration versus distance from first sample location in the Samé towndrain.

5 Discussion


5 DISCUSSION

The ultimate objective of this study was to collect baseline water quality data for the Samé

river system. This data is discussed below in the context of: any similarities in the results on a

regional level; the extent of the nutrient load in the system and whether it is delivered to the

coastal environment; and the quality of local water supplies and how they are affected by

surrounding land use. A review of the methodologies employed during the field trip and

recommendations as to their usefulness and appropriateness is also included.

5.1 Regional Similarities

The Samé river system was compared to other rivers in the region. With respect to its physical

parameters the Samé system appears representative of the tropical rivers in southeast Asia.

pH levels were close to neutral in the Samé, Ciliwung and Sunter river systems. The salinity

levels in these rivers, as represented by electrical conductivity (EC) were all less than 1990

S/cm which is typical of freshwater bodies (Waterwatch Australia Steering Committee

2002). The low EC in the Samé system implies that there is minimal salt input from either

groundwater or runoff in the catchment, or from the ocean. This might be expected during the

dry season because of low rainfall, low runoff and minimal connection with the ocean at the

river mouth. Surface water temperatures were also in a similar range with the exception that

the variability in the Samé system was much higher than that in the Indonesian rivers. Only 5

measurements were taken in the Indonesian rivers compared to the 24 taken in Samé. This

may account for the higher variability. The slightly lower average in Samé may also be due to

the forest cover which was observed at a majority of sample sites (Appendix E). Forest cover

provides shading from the sun, thus lowering the temperatures of the surface water (Brooks et

al. 1997). In contrast, the area surrounding Jakarta and the Ciliwung and Sunter rivers is

continually being deforested (Energy Information Administration U.S. Government 2004).

The lack of forest cover might account for the higher temperatures in these rivers. From these

findings it may be concluded that the Same river system shares physical characteristics with

other rivers in the region. This has implications when considering any variation in nutrient or

chlorophyll a concentrations. Any differences observed in nutrient or chlorophyll a are

therefore, not likely to be due to differences in the physical characteristics of the rivers.

5 Discussion


5.2 Nutrient Inputs and Riverine Delivery to the Coast

Nutrients are one the most common pollutant of natural waterways (Wetzel 2001). The Samé

river system contained significantly less nutrient loads than other rivers in the region. Nitrate,

nitrite, phosphate and ammonium were all below detection limits (see Section 3:

Methodology). At most, the Samé system contained half the level of nutrients of the Ciliwung

and Sunter rivers. This variation could be linked to differences in catchment management

practices. Land use is considerably more intensive in the Jakarta area of Indonesia.

Deforestation for agriculture and waste disposal from both domestic and industrial activities

occur along the lengths of the Ciliwung and Sunter rivers (Energy Information Administration

U.S. Government 2004; Palupi et al. 1995). Palupi et al. (1995) link such activities to high

levels of nutrients. The high quality of water discharged from forested watersheds is well

known (Food and Agriculture Organisation of the United Nations Forestry Department 2003).

Riparian forests help to stabilise stream banks, reduce runoff into water bodies from upland

areas and maintain cooler water temperatures. These improve dissolved oxygen levels in

water and thus decrease nutrient release into the water column (Brooks et al. 1997). Forests

also efficiently cycle nutrients and chemicals and decrease the sediment exported, thus

reducing pollutants such as phosphorus and some heavy metals. The Samé river catchment

supports basic subsistence agriculture by a small population and TimorLeste maintains a

higher area of forest per capita and approximately half the deforestation rates of Indonesia

(Food and Agriculture Organisation of the United Nations Forestry Department 2003). It is

likely that the differences in catchment management practices account for the low nutrient

load in the Same river systems.

One way of characterising aquatic systems is by using trophic status – that is the level of

productivity of the system (Wetzel 2001). The results for the Samé river suggest that it is

currently an oligotrophic system. Indicators of an oligotrophic river system include sestonic

chlorophyll concentrations of less than 10 mgl1 of total phosphorus concentrations less than

25 gl1 and total nitrogen concentrations less than 700 gl1 Wetzel (2001). While it was not

possible to determine total phosphorus and nitrogen concentrations, chlorophyll a levels

measured were all below 10 mgl1. The Organisation for Economic Cooperation and

Development (1982) also define oligotrophic freshwater systems as containing less than 1.7

mgm3 (= 1700 mgl1) of average chlorophyll a, less than 8 mgm3 (= 8000 mgl1) of total

average phosphorus and less than 661 mgm3 (= 661 000 mgl1) of total average nitrogen. The

ANZECC/ARMCANZ guidelines (2000) define an oligotrophic system as containing less

than 2 gl1 of chlorophyll a. The Samé river system met all these criteria, so it may be

5 Discussion


concluded that it is oligotrophic during the dry season and thus no significant nutrient loads

were present at the time of sampling.

The assimilative capacity of a river system increases with increasing flow rates (Water and

Rivers Commission 1999). This is because of the larger volumes of water that dilute any

nutrients present in the water column. When sampling was undertaken, monsoonal rains had

ended approximately two weeks earlier and water levels had significantly decreased in the

rivers (Dr Warwick Crowe, pers. comm., 26/06/2004). During the wet season nutrient and

chlorophyll a levels may be lower due to the greater assimilative capacity of the river system

with rainfall increasing flow rates . This pattern was observed in both the Ciliwung and Sunter

rivers (Palupi et al. 1995).

Algal growth in freshwater systems is typically limited by phosphorus (Organisation for

Economic Cooperation and Development 1982). Further evidence for the low total nutrient

load in the Samé system was the low chlorophyll a concentrations in the water. When large

amounts of limiting nutrients, such as nitrogen and phosphorus enter water systems, the

growth of primary producers increases. This is due to an increased carrying capacity from the

widespread availability of these limiting nutrients (Fennessy 2004). Total nitrogen and

phosphorus loads in the Samé river system were unknown as it was not possible to test for

these. However, low chlorophyll a levels which represent the biomass of primary producers

suggests that they are, indeed, low. This, and the other findings above, suggests that the Samé

river system contributes minimal nutrient loads to the coastal environment during the dry

season. This information is important in the context of future development. There are a

number of developments suggested for the Same river catchment. An increase in intensive

agriculture, tourism, the possible construction of a marina and further onshore oil and gas

exploration in the area may all affect riverine inputs to the coast. At present these inputs are

most likely very low. Therefore such developments have the potential to significantly impact

upon both the riverine and coastal environment.

5 Discussion


5.3 Local Water Quality

The cumulative effects of human activity in direct proximity to local water resources can

manifest as an increase in algal biomass (Freedman 1995). One of the aims of this study was

to determine the causes of any local scale variation in water quality. The water quality

decreased slightly, yet significantly, in a diversion channel supplying the town of Same. This

channel diverts water from the same source as the main Same river system. It runs along the

side of the major road in the town. Animals, animal faeces and rubbish were frequently

noticed in and near the drain water (Appendix E). People were often observed to wash in the

drain water which was also used for cooking and drinking. Chlorophyll a was found to

increase significantly with distance downstream of the drain. This suggests that it is likely that

human and animal activity is a source of nutrients in the local drain water.

Local tap water was found to contain similar levels of chlorophyll a to the channel. This

suggests that local water is not treated before distribution. However, chlorophyll a levels were

still considered relatively low according to the ANZECC/ARMCANZ (2000) guidelines. The

guidelines recommend that drinking water should contain less than 0.005mg/l (= 5 g/l). The

average level of chlorophyll a in the local tap water was only 0.5 g/l. This suggests that the

tap water which is sourced from the same location as the Samé river is relatively pristine,

even thought it is affected by anthropogenic influences and not treated before distribution.

5.4 Assessment of Methodology

The appropriateness and usefulness of the techniques employed in TimorLeste were

considered. The suitability, practicality, cost and resolution of each method is discussed

below. Table 51 displays the outcomes of these considerations for the methods used during

the field trip.

Methodologies employed on any field trip should be suited to the conditions of the study site.

Not all scientific methods utilised in developed nations are applicable in poorer regions of the

world. This is demonstrated by Chandra et al. (1990) who illustrated that “state of the art”

techniques were not suitable for water development projects in poorer countries, and that an

appropriate design had to be determined by considering each study site individually. The lack

of resources in TimorLeste limited the choice of methods to be used during the field

operation in Samé. The lack of power, chemicals and waste disposal facilities meant that

5 Discussion


simple techniques using equipment that did not depend on electricity or an extensive chemical

supply had to be used. To ensure that Timorese staff and students could assist with sampling

and analytical techniques that they had not previously come across, methods were required to

be simple and easy to understand. The YEOCAL probe was suitable for this criteria as it was

batteryoperated, did not require any additional chemicals or equipment and only required

basic reading from an electrical monitor to determine results. Test strips are suggested by the

United Nations Environment Programme and World Health Organisation (1996) as a

appropriate method for testing water quality. This method was also found to be suitable for

nutrient and metal analyses in TimorLeste. Basic kits were available for a variety of chemical

tests and were small, easily carried packages containing all necessary equipment for analyses.

The process for analysis was a basic colorimetric test that did not require extensive training or

reading and writing skills to use. This is an important consideration as the adult literacy rate

for TimorLeste is only 58.6% (United Nations Development Programme 2004). Therefore

this technique can be utilised by a larger group of people. In contrast, a high resolution

technique that was originally considered required sensitive glassware and a variety of

chemicals for analysis (Parsons et al. 1984). As a result, it was also a significantly more

involved and delicate procedure and so it was not deemed suitable for field work in Timor

Leste.

Table 51: A comparison of the cost, practicality, simplicity, waste disposal requirements and analyticalresolution of water quality assessment techniques.

Parameters Cost ($) Practicality Simplicity WasteDisposal

Analysis

Chemical(Standard

Lab.)

Freight:~$500 $2000

Extremelydifficult to

import

Complexmethod

Difficult Highresolution

Chemical(Test strips)

~$0.50$2/sample

Easilyimported

Simplemethod

Easy Lowresolution

Biological(Chlorophyll

a)

~$16000AUD

Localmaterialsavailable

Simplemethod

Easy Highresolution

Physical(Yeocal)

~$9000AUD

Easilycarried

Simplemethod

None Highresolution

The chlorophyll a extraction by sonication and fluorometric technique was also found to be a

relatively simple method requiring little equipment for use. Plastic equipment was sufficient

for filtration and shaking by hand sufficient for sonication. Only two basic chemicals (ethanol

and hydrochloric acid) and a fluorometer, which was small enough to transport in hand

5 Discussion


luggage on the plane, were necessary for analysis. Although slightly more sophisticated than

the test strip analyses, this method was still less complicated than other biological assessments

such as pigment analysis using high performance liquid chromatography (Wetzel and Likens

2000). When considering the suitability of the methods used during the field trip in Timor

Leste, all three techniques: in situ YEOCAL measurements, test strip analyses and

chlorophyll a extraction and fluorometric analyses, were found to be relatively simple,

requiring minimal equipment and thus easily taught and conducted (Table 51).

The practicality of conducting sampling and analyses had to be considered for a successful

field trip. Transporting hazardous chemicals, a lack of materials and a lack waste disposal

facilities were major considerations in choosing analytical techniques. As examined in the

Chapter 3: Methodology, well planned, costly customs certification was required to transport

the chemicals necessary for high resolution nutrient and metal analyses (highlighted in red in

Table 51). The facilities required to dispose of such hazardous chemicals were also not

guaranteed in TimorLeste. In comparison, test strips did not contain materials classified as

“dangerous goods” and could be easily imported and disposed of. Although ethanol and

hydrochloric acid did have to be freighted separately to Dili for chlorophyll a analysis, it was

later discovered that acid and acetone (which is a suitable replacement for ethanol (Papista et

al. 2002)) were locally available. Acid could be obtained from the Department of Energy and

Mineral Resources while acetone could be bought from a major hardware store in Dili. The

substances were also easily disposed of by diluting waste with large volumes of water into a

septic tank system. Waste disposal and importation were not issues that affected the use of the

YEOCAL probe. In summary, test strip and chlorophyll a analyses involved materials that

could be easily imported and disposed of and were thus practical methods for the field trip in

TimorLeste.

The level of sophistication of analysis employed during the field trip was limited by the cost

of conducting each method. It is shown in Table 51 that the analysis of a sample costs $0.5

$2 per test strip (EnviroEquip 2004). Therefore, the maximum cost of using test strips during

the field trip was $96 (48 samples x $2). No additional cost was necessary for importing the

strips. In comparison, to freight a limited range of chemicals could cost anywhere between

$500$2000 (Dangerous Goods Management, pers. comm., 07/06/2004), depending on the

number and quantity of chemicals being imported. As materials are locally available for

chlorophyll a analysis, no ongoing cost would be necessary for importing chemicals.

However, a major outlay of approximately $16 000 was necessary for the fluorometer (Dr

5 Discussion


Anya Waite, pers. com., 09/10/2004). The YEOCAL probe cost approximately half this

amount at around $7000 (Dr Anya Waite, pers. com., 09/10/2004). In summary, the test strips

were the cheapest methodology used during the field trip by far. While equipment for

chlorophyll a and physical measurements required a high upfront cost, no ongoing costs for

importing materials would be necessary for these methods.

The methods used in TimorLeste varied in resolution resulting in a distinction in the

accuracy of the data, as presented in Chapter 4: Results. While test strip analyses were only

semiquantitative in resolution, they still provided a reasonable indication of the presence of a

significant nutrient load. The ANZECC/ARMCANZ (2000) guidelines define the nutrient

trigger values (at which water bodies risk adverse affects from nutrients) for tropical lowland

rivers as: 10 g/l for total phosphorus, 200300 g/l for total nitrogen and 10 g/l for both

nitrates and ammonium. The test strips used during the field trip could only provide a

distinction for phosphate and ammonium concentrations greater than 3 mg/l (3000 g/l), and

nitrate concentrations greater than 0.5 mg/l (500 g/l). Thus it was not possible to determine

whether river water contained nutrient concentrations at levels greater than these trigger

values. However, significantly higher levels of pollution, as present in some Indonesian rivers

(Palupi et al. 1995), would have effectively been observed using this method. In contrast, the

trigger value for chlorophyll a levels in tropical lowland rivers is defined as 5 g/l in the

ANZECC/ARMCANZ (2000) guidelines. As previously mentioned, all chlorophyll a

measurements were lower than this value indicating the healthy status of the Samé river

system. While the resolution of the chlorophyll a analysis was able to discern this condition,

the test strip method could not.

In summary, test strip analyses were a cheap, simple and practical method that could be easily

repeated and taught to a majority of people. The test strips could successfully identify major

sources of nutrients, however, the resolution of this method was too low to observe

differences in concentrations in oligotrophic environments such as the Samé river system.

Their application may be more useful in areas where pollution is likely to be more intense,

such as around the larger cities of Dili and Baucau. In contrast, whilst relatively more

expensive to conduct, chlorophyll a analyses was also practical yet able to distinguish

concentrations and thus the quality of water at a much greater level of accuracy. Thus, this

method was considered more applicable in the Samé region due to the pristine quality of the

river system there. Measurements using the YEOCAL probe were also found to be a simple

and easily applied method for testing the in situ physical conditions of a water body.

6 Conclusions


6 CONCLUSIONS

From the results it appears that the Samé river system is a relatively pristine oligotrophic

environment during the dry season. The water quality status might improve during the wet

season when the assimilative capacity of the river increases with rain water. By examining

other physically similar rivers in the region it was suggested that nutrient levels and algal

growth would decrease in the wet season. Further monitoring is necessary to determine the

extent of such an decrease. It was also suggested that land use around a riverine system

influences the nutrient inputs to that system. This was confirmed by the less intensive land use

and lower nutrient load in the Samé river system when compared to other Indonesia rivers.

From these findings it was concluded that the Samé river system contributes a minimal

nutrient load to the south coastal region of TimorLeste during the dry season.

The influence of land use and human activity on water quality was also demonstrated at a

local scale in Samé. Water quality as represented by chlorophyll a increased downstream of

the local water supply. Human and animal activity was considered a likely source of nutrient

input which may have caused the increase in chlorophyll levels. Tap water also maintained

similarly low levels of chlorophyll a to the natural rive system. This implied that the tap water

was not treated before distribution, however, was of an adequate quality for human use due to

confirming the pristine quality of the Samé river system.

It was concluded that the test strip analyses were a cheap, effective method for observing

significant nutrient loads in rivers in TimorLeste. In comparison, fluorometric chlorophyll a

analysis was a better technique for investigating changes in water quality in more pristine

environments such as the Samé river system. Both techniques were found suitable for the

region and practical in design for their application in a developing nation. Such methods are

recommended to continue monitoring over a variety of temporal and spatial scales in the

Samé river system. In this way any changes in water quality might be more effectively

investigated, especially in the context of future development which might impact upon the

pristine Samé riverine system.

7 Recommendations


7 RECOMMENDATIONS

Based upon the results of this study, recommendations for future research to the Government

of TimorLeste and scientific community include:

• To continue an intensive water quality monitoring program in Samé based on

similar parameters in this study. This study was only conducted at one point in

time at the beginning of the dry season. It is important to test water quality over

both a spatial and temporal scale to understand changes in water quality that occur

under different conditions. Therefore to obtain an adequate collection of baseline

data it will be necessary to monitor water quality over both the wet and dry

seasons at a variety of spatial scales. In the context of future development this is

particularly crucial in order to observe any changes in river conditions, and thus

more effectively minimise any environmental impacts that might occur.

• To further investigate the delivery of riverine nutrient inputs to the coast in the

Samé region. Potential nutrient sources should also be identified and the effect that

the geography of the area (e.g. tropical conditions, pH, temperature and seasons)

has on these, in both the wet and dry seasons. Again, this is crucial in the context

of future development in the area which might impact upon the ecological health

of the riverine and coastal systems.

• To employ fluorometric chlorophyll a techniques, test strip analyses and basic in

situ physical measurements as the primary methods for water quality assessment.

Test strip analyses were found to be a cheap and effective method of determining

the presence of significant nutrient and metal inputs in rivers. This method would

be especially effective in observing any significant pollution in water bodies

around more populated areas, such as Dili or Baucau. Chlorophyll a analyses

provided higher resolution data which would be most effective in assessing

changes in water quality in more pristine environments, such as rivers in the Samé

region.

• To create a Water Quality Manual based upon the techniques employed in this

study. By summarising the recommended methods used in this project into a

booklet form, monitoring techniques can be taught and performed more effectively

and consistently. Therefore any data collected by different groups would also be

7 Recommendations


more consistent in accuracy and resolution, and any patterns might be more readily

discerned.

• To implement a program coordinating the collection of environmental data from

both the Government and TimorLeste and NonGovernment Organisations

(NGOs). To ensure a more efficient use of resources and more effective nation

wide monitoring, a team or program should be implemented to liaise between

environmentallyconcerned NGOs (such as Ozgreen) and the Department of

Environmental Services. Specifically, this will assist in improve the collection of

water quality data and assist in the improvement of water resource management

plans.

• To train the local population in environmental monitoring and gain local

knowledge from them. By encouraging such programs, local communities can

contribute valuable information that might otherwise be difficult to obtain. Local

involvement is pertinent to maintaining healthy sustainable ecological systems

such the Samé river system.

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9 Appendices


9 APPENDICES

APPENDIX A: GLOBAL POSITIONING SYSTEM (GPS) DATA

Table 91: Coordinates of each sample site (R1R24)

SiteLat Deg

Lat Min

Long Deg

Long Min Site Lat Long

R1 9 5.751 125 41.538 R1 9.09585 125.6923R2 9 5.785 125 41.635 R2 9.09642 125.6939R3 9 5.378 125 41.174 R3 9.08963 125.6862R4 9 10.568 125 41.359 R4 9.17613 125.6893R5 9 0.947 125 38.933 R5 9.01578 125.6489R6 8 59.547 125 39.251 R6 8.99245 125.6542R7 8 59.377 125 39.449 R7 8.98962 125.6575R8 8 58.858 125 37.426 R8 8.98097 125.6238R9 9 0.584 125 38.887 R9 9.00973 125.6481R10 9 0.284 125 38.942 R10 9.00473 125.649R11 8 59.893 125 38.803 R11 8.99822 125.6467R12 8 59.55 125 38.092 R12 8.9925 125.6349R13 9 4.002 125 41.755 R13 9.0667 125.6959R14 9 3.724 125 41.74 R14 9.06207 125.6957R15 9 3.861 125 41.926 R15 9.06435 125.6988R16 9 2.644 125 40.09 R16 9.04407 125.6682R17 8 55.955 125 37.88 R17 8.93258 125.6313R18 8 56.058 125 36.569 R18 8.9343 125.6095R19 8 59.597 125 41.416 R19 8.99328 125.6903R20 9 0.035 125 38.938 R20 9.00058 125.649R21 9 6.272 125 41.545 R21 9.10453 125.6924R22 9 5.099 125 41.534 R22 9.08498 125.6922R23 9 10.726 125 41.3 R23 9.17877 125.6883R24 9 10.769 125 41.288 R24 9.17948 125.6881

9 Appendices


APPENDIX B: RAW PHYSICAL DATA

Saturday 26/06/04 (9:30am 1pm) ALL AVERAGES CALCULATED FROM 3 OR MORE VALUES

SampleTemp (degC) pH Sal (ppt) Ave

Width(m) Ave Flow

T1 T2 T3TAVE pH1 pH2 pH3 pH AVE S1 S2 S3 S AVE Depth (m)

Vel(m/s) (m^3/s)

R1 23.01 23.02 23.04 23.02 6.85 6.98 6.97 6.93 0.25 0.46 0.46 0.39 0.12 9 0.25 0.27R2 22.11 22.11 22.1 22.11 6.64 6.51 6.53 6.56 0.42 0.42 0.42 0.42 0.2 28 0.65 3.65R3 26.78 26.78 26.78 26.78 6.74 6.67 6.7 6.70 0.44 0.44 0.44 0.44 0.24 9 0.67 1.44

NOTE:DO probe not working (DO(mg/L) = 0.4, DO% sat = 10)TURB (ntu) = 1.0 for all sample sites (maybe not working)

Sunday 27/06/04 (3:30pm 5:30pm)


Width(m) Ave Flow Ave

pHPaper


Vel(m/s) (m^3/s) EC

R5 23.24 23.21 23.19 23.21 6.95 7.02 7.06 7.01 0.44 0.44 0.44 0.44 0.12 6.7 0.83 0.66 0.35 7R6 22.75 22.74 22.74 22.74 7 7.12 7.14 7.09 0.39 0.39 0.39 0.39 0.15 4 0.67 0.40 0.2 7R7 22.32 22.32 22.31 22.32 7.24 7.32 7.33 7.30 0.4 0.4 0.4 0.4 0.08125 5.8 0.60 0.28 0.3R8 18.77 18.77 18.76 18.77 7.1 6.94 6.95 7.00 0.44 0.44 0.44 0.44 0.1625 7.5 0.35 0.43 0.3

Monday 28/06/04 (10am 11am)



pHPaper


Vel(m/s) (m^3/s) EC

9 Appendices


R9 20.56 20.51 20.42 20.50 6.75 6.7 6.7 6.72 0.41 0.41 0.41 0.41 0.0475 0.95 3.13 0.14 0.25 7R10 20.5 20.49 20.45 20.48 6.76 6.8 6.83 6.80 0.41 0.41 0.41 0.41 0.08 0.94 1.54 0.12 0.2R11 20.02 20.02 20.01 20.02 7 6.98 7 6.99 0.41 0.41 0.41 0.41 0.38 1.05 0.81 0.32 0.2R12 19.64 19.63 19.61 19.63 7.04 6.96 6.92 6.97 0.42 0.42 0.42 0.42 0.29 1.43 1.17 0.49 0.25 7

NOTE: These samples all from drains in Samé town (R9 = bottom end of town, R12 = top end of town, R8 = Source of Samé water)

Tuesday 30/06/04 (2:45pm)



pHPaper


Vel(m/s) (m^3/s) EC

R4 27.94 27.9 27.9 27.91 6.4 6.24 6.16 6.27 0.44 0.44 0.44 0.44 0.10 3 0.81 0.25 0.3 7

NOTE: These parameters were tested ~4 days after samples were taken.The creek where sampling occurred is one section of a wide river and not necessarily representative of quality at this location.E.g. Water at R4 was clear but >20m downstream water was much more turbid.

Saturday 03/07/04 (9:45am 1:15pm)



pHPaper


Vel(m/s) (m^3/s) EC

R13 22.09 22.1 22.09 22.09 7.13 7.15 7.17 7.15 0.41 0.41 0.41 0.41 0.5025 15 1.05 7.88 0.25R14 22.49 22.46 22.46 22.47 6.95 6.96 6.98 6.96 0.39 0.4 0.4 0.40 0.32 16.5 0.9375 4.94 0.2R15 27.02 27 26.99 27.00 7.02 7.01 7.03 7.02 0.42 0.42 0.42 0.42 0.21 13 1.56 4.18 0.3 7R16 24.72 24.71 24.71 24.71 6.86 6.87 6.88 6.87 0.49 0.49 0.49 0.49 0.17 3 0.30 0.16 0.45

Wednesday 07/07/04 (3pm 5:30pm)



pHPaper

T1 T2 T3 T pH1 pH2 pH3 pH AVE S1 S2 S3 S AVE Depth (m) Vel (m^3/s) EC

9 Appendices


AVE (m/s)R17 18.91 18.9 18.9 18.90 6.97 6.97 7 6.98 0.42 0.42 0.42 0.42 0.17 3.9 0.38 0.25 0.25R18 17.09 17.09 17.08 17.09 7.05 7.06 7.08 7.06 0.44 0.44 0.44 0.44 0.275 1.3 0.17 0.06 0.3R19 22.08 22.09 22.09 22.09 7.45 7.47 7.47 7.46 0.41 0.41 0.41 0.41 0.29 21 0.82 4.98 0.3

Thursday 08/07/04



pHPaper


Vel(m/s) (m^3/s) EC

R20 23.66 23.65 23.63 23.65 7.01 7.03 7.05 7.03 0.39 0.39 0.39 0.39 0.00008 0.3R21 26.21 26.2 26.19 26.20 7.14 7.15 7.15 7.15 0.4 0.4 0.4 0.4 0.38 23.4 1.05 9.29 0.3R22 25.53 25.53 25.52 25.53 7.01 7.02 7.05 7.03 0.41 0.41 0.41 0.41 0.31 30 1.50 14.03 0.3

NOTE: R20 samples were taken from tap water at the back of the house.Physical measurements were taken in a round black bucket, after water ran for ~2hrs (D = 0.42m, Ht = 0.18m)Flow measured, as average time taken to fill a 1.5L water bottle

Friday09/07/04



pHPaper


Vel(m/s) (m^3/s) EC

R23 22.98 22.97 22.97 22.97 6.91 6.89 6.9 6.90 0.41 0.41 0.41 0.41 0.14 3.2 0.51 0.22 0.3R24 22.97 22.95 22.94 22.95 6.79 6.77 6.78 6.78 0.41 0.41 0.41 0.41 0.21 12 1.39 3.48 0.25

NOTE: Above sampling taken as part of crosssection at location near river mouth (with R4)

9 Appendices


APPENDIX C: RAW CHEMICAL DATA

Saturday 26/06/04 (9:30am 1pm)

Test Strip Analyses: Sunday (9am 1pm)

SampleNO3(mg/L) NO2 (mg/L)

Cu(mg/L)

Fe(mg/L) Ag (µg/L)

R1a 0.5 0 0.5 0 0R1b 0.5 0 0 0 0R2a 0.5 0 0 0.02 050R2b 0.5 0 0 0 050R3a 2 0 0 0.02 0R3b 0.5 0 0.5 0 0R4a 0.5 0 0 0 0R4b 0.5 0 0 0 050R4c 0.5 0 0 0.02 050

Test Strip Analyses: Thursday (1:30pm 5:30pm)

SampleNH4+(mg/L)

PO43(mg/L)

R1a 0 03R1b 0 0R2a 0 0R2b 0 03R3a 0 0R3b 0 03R4a 0 0R4b 0 03R4c 0 03

Sunday 27/06/04 (3:30pm 5:30pm)

Test Strip Analyses: Sunday (5:30 pm 7:30pm)


Cu(mg/L)

Fe(mg/L) Ag (µg/L)

R5a 0.5 0 0 0 050R5b 0.5 0 0 0 050R6a 0.5 0 0 0 0R6b 0.5 0 0 0 050R7a 0.5 0 0 0 050R7b 0.5 0 0 0.02 050R8a 0.5 0 00.5 00.02 050R8b 0.5 0 00.5 0 50

NOTE: Under fading light/veranda light bulb


9 Appendices


SampleNH4+(mg/L)

PO43(mg/L)

R5a 0 0R5b 0 0R6a 0 0R6b 0 03R7a 0 03R7b 0 03R8a 0 0R8b 0 0

Monday 28/06/04 (10am 11am)

Test Strip Analyses: Monday (4:30pm 6:30pm) & Tuesday (10:30am 11:00am)


Cu(mg/L)

Fe(mg/L) Ag (µg/L)

R9a 0.5 0 0 0 050R9b 0.5 0 0 0 050R10a 0.5 0 0 0 0R10b 0.5 0 0 0 0R11a 00.5 0 0 0 0R11b 0 0 00.5 0 0R12a 0.5 0 0 0 0R12b 0.5 0 0 0 050

NOTE: These samples all from drains in Samé town (R9 = bottom end of town,R12 = top end of town,R8 = Source of Samé water)


SampleNH4+(mg/L)

PO43(mg/L)

R9a 0 0R9b 0 0R10a 0 0R10b 0 0R11a 0 0R11b 0 0R12a 0 0R12b 0 0

Saturday 03/07/04 (9:45am 1:15pm)

Test Strip Analyses: Sunday (10:30am 1pm)


Cu(mg/L)

Fe(mg/L) Ag (µg/L)

NH4+(mg/L)

PO43(mg/L)

R13a 00.5 0 0 0 0 0 03R13b 00.5 0 00.5 0 0 0 3R14a 00.5 0 0 0 0 0 0R14b 00.5 0 0 0 0 0 0R15a 00.5 0 0 0 0 0R15b 00.5 0 0 0 0 0R16a 00.5 0 0 0 0 0 0R16b 00.5 0 0 0 0 0 0

9 Appendices



Test Strip Analyses: Friday (8:45am 10:00am)


Fe(mg/L)

Ag(µg/L)

R17a 0.5 0 0 0R17b 00.5 0 0 050R18a 00.5 0 0 050R18b 00.5 0 0 0R19a 00.5 0 0 0R19b 00.5 0 0 0

Test Strip Analyses: Saturday (8:00am 10:00am)

SampleNH4+(mg/L)

PO43(mg/L)

R17a 0 03R17b 0 03R18a 0 0R18b 0 0R19a 03 0R19b 0 0

Thursday 08/07/04 (1pm 5:30pm)

Test Strip Analyses: Friday (8:45am 10:00am)


Fe(mg/L)

Ag(µg/L)

R20a 0.5 0 0 050R20b 0.5 0 0 0R21a 00.5 0 00.02 0R21b 00.5 0 00.02 0R22a 00.5 0 0 0R22b 00.5 0 0 0


SampleNH4+(mg/L)

PO43(mg/L)

R20a 03 0R20b 03 0R21a 0 03R21b 0 0R22a 0 0R22b 0 0

Friday 09/07/04 (9:40am 10:30am)



Fe(mg/L)

Ag(µg/L)

NH4+(mg/L)

PO43(mg/L)

9 Appendices


R23a 00.5 0 0 0 0 0R23b 0.5 0 0 050 0 0R24a 0.5 0 0 0 0 03R24b 0.5 0 0 0 0 03

9 Appendices


APPENDIX D: RAW BIOLOGICAL DATA

Saturday 26/06/04 (9:30am 1pm)

Sample V (L filtered) v (L extract)Rb(µg/L)

Ra(µg/L)

Chl a(µg/L)* Average

R1a 0.23 0.008 7 4.7 0.147 0.154R1b 0.2 0.008 4.6 2.4 0.161R2a 0.2 0.008 4.6 3.2 0.103 0.124667R2b 0.2 0.008 5.4 3.4 0.147R3a 0.2 0.008 2.4 1.8 0.044 0.047667R3b 0.2 0.008 2.3 1.6 0.051R4a 0.2 0.008 48.2 25.6 1.657 1.802533R4b 0.2 0.008 46.6 23.3 1.709R4c 0.25 0.008 76.1 41.3 2.042

Sunday 27/06/04 (3:30pm 5:30pm)


Ra(µg/L)


R5a 0.24 0.008 18.8 10.5 0.507 0.337944R5b 0.2 0.008 7.4 5.1 0.169R6a 0.2 0.008 5 3.8 0.088 0.088R6b 0.2 0.008 4.5 3.3 0.088R7a 0.2 0.008 7.3 5 0.169 0.187R7b 0.2 0.008 7.5 4.7 0.205R8a 0.2 0.008 0.8 0.6 0.015 0.022R8b 0.2 0.008 1.1 0.7 0.029

Monday 28/06/04 (10am 11am)


Ra(µg/L)


R9a 0.4 0.008 26 15.1 0.400 0.454667R9b 0.4 0.008 32.5 18.6 0.510R10a 0.4 0.008 16.2 9.7 0.238 0.293333R10b 0.4 0.008 20.6 11.1 0.348R11a 0.2 0.008 7.6 5.1 0.183 0.179667R11b 0.2 0.008 7.5 5.1 0.176R12a 0.4 0.008 3.2 2.2 0.037 0.058667R12b 0.4 0.008 5.5 3.3 0.081

NOTE: These samples all from drains in Samé town (R9 = bottom end of town,R12 = top end of town, R8 = Source of Samé water)

All samples above were measured on Friday 02/07/04 at ~11am.

Saturday 03/07/04 (9:45am 1:15pm)


Ra(µg/L)


R13a 0.41 0.008 37.6 20.3 0.619 0.652661R13b 0.485 0.008 51.3 28.6 0.686R14a 0.43 0.008 58.7 33.1 0.873 0.818647R14b 0.405 0.008 49.0 27.9 0.764

9 Appendices


R15a 0.39 0.008 10.1 6.7 0.128 0.132847R15b 0.415 0.008 11.5 7.6 0.138R16a 0.41 0.008 41.8 23.2 0.665 0.618495R16b 0.39 0.008 33.2 18.0 0.572

Above samples were measured on Monday 05/07/04 at ~10:30am



Ra(µg/L)


R17a 0.42 0.008 51 30.6 0.712 0.567857R17b 0.44 0.008 31.1 18.4 0.423R18a 0.465 0.008 66.8 46.4 0.643 0.538787R18b 0.25 0.008 19.3 11.9 0.434R19a 0.45 0.008 22.1 10.6 0.375 0.302907R19b 0.4 0.008 15.2 8.9 0.231

Thursday 08/07/04 (1pm 5:30pm)


Ra(µg/L)


R20a 0.395 0.008 44.2 24.3 0.739 0.501809R20b 0.41 0.008 17.2 9.8 0.265R21a 0.4 0.008 12 7.6 0.161 0.278667R21b 0.4 0.008 21.4 10.6 0.396R22a 0.4 0.008 29.1 16.1 0.477 0.4785R22b 0.4 0.008 26.8 13.7 0.480

Friday 09/07/04 (1pm 5:30pm)


Ra(µg/L)


R23a 0.4 0.008 39.6 23 0.609 0.597667R23b 0.36 0.008 28 13.6 0.587R24a 0.39 0.008 50.7 32.8 0.673 0.639081R24b 0.4 0.008 41 24.5 0.605

Above samples were measured on Sunday 10/07/04 at ~10:00am in Timor LodgeroomTravelled in bus, in esky with no ice ~5 hours. Perhaps degraded.

*Chl a = (r/(r 1)(Rb Ra)(v/V)r = 2.2

9 Appendices


APPENDIX E: SAMPLE SITE NOTES, OBSERVATIONS AND

PHOTOGRAPHY

~ 1 2 weeks before arriving monsoonal rains were still occurring, rivers were almost full, asthe wet season ended late this year

Saturday: 26/06/04

Present:• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Dr Grey Coupland• Halinka Lamparski.

Location: R1• Time: 9.30am• Samples from Mota Ajasso, just north of junction with Mota Caraulun• GPS: S 09º 05.751’

E 125º 41.538’• Sunny, some clouds• Tall Casuarina trees (~10m) surrounding sample site (akin to firs), and bushes (no signs of

clearing or intensive agriculture)• Wide, rocky gravel river• Partially turbid water, could see major rocks, fast flowing• Only section of fully flowing river (in wet season)• Some green algal growth on banks• Animal tracks all around banks into water• People washing clothes on opposite side of river• Near to main road, southwest (R3) and southeast (R2) of 2 major bridges

Source: Dr Grey Coupland 2004.

Location: R2• Time: 10:12am• Samples from Mota Caraulun, just north of junction with Mota Ajasso and ~100m south

of major bridge• GPS: S 09º 05.785’

E 125º 41.635’• More overcast, light breeze• Wider, faster flowing section of river

9 Appendices


• Very turbid, no visibility (perhaps from metamorphic mudstone/carbonaceous (plantmatter) rock in range from which river originated (Lolotoi Complex; in Kablaki Range nthof Samé), pers comm Dr Warwick Crowe)

• Only section of fully flowing river (in wet season)• Across from 2 dilapidated houses• People washing downstream• Animals observed in surrounding area


Location: R3• Time: 1pm• Samples from Mota Ajasso, ~100m north of major bridge and ~0.5km north of

intersection with Mota Caraulun.• GPS: 09º 05.378’

125º 41.174’• Overcast, light breeze• Partially turbid, slower flowing, shallow water• Vegetation on one side of bank• Creek is part of a section (~9m) of original wide gravel river (in wet season ~50m)


Sunday: 27/06/04

Present:• Mr Egidio• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Dr Grey Coupland• Halinka Lamparski.

Location: R5• Time: 3:30pm• Samples from Mota Caloco, southeast of Samé• GPS: S 09º 0.947’

9 Appendices


E 125º 38.933’• Overcast• Greyish water, but can see the bottom of the stream• Big rocks/boulders & gravel, minirapids• Brown algae in patches• ~10m west of bridge crossing from Samé to Betano• Animal faeces next to bank & surrounding area, indication of presence of animals• People seen working/moving in and next to river• ~23m high banks (some erosion) & limestone rocks• High cliffs nearby• Thick vegetation and trees, some overhanging


Location: R6• Time: 4:16pm• Samples from Mota Ermetin, northeast of Samé• GPS: S 08º 59.547’

E 125º 39.251’• Slower (than R5) flow and clearer water (not completely)• Small rocky, gravel river• Next to coffee plantations• Near 2 occupied dilapidated houses, people’s washing on log next to site• Tall tropical trees & bush, further up in the mountains (higher than Samé)• Directly next to bridge/road crossing (built by Portuguese)


Location: R7• Time: 4:36pm• Samples from Mota Abatu (between Mota Ermetin & Mota Caraulun; not on map)• GPS: S 08º 59.377’

E 125º 39.449’• Clear water over finer gravel, some rocks and leaf litter• Some rubbish (plastics etc) in water

9 Appendices


• Lush & cool, large overhanging trees & vines• Horse on road directly above & next to river (river lower & next to road)


Location: R8• Time: 5:18pm• Samples from Mota Oilala (between Mota Caloco & Mota Ermetin; not on map)• GPS: S 08º 58.858’

E 125º 37.426’• Clear water over stone waterfall, in the mountains of Kablaki: SOURCE of water for town

of Samé• Samples taken in section of slower flowing water, compared to strong waterfall currents

above and below sample site• Lush, beautiful tall forest & veg, completely overhanging (dark) & cool• Very few people and animals (upstream of main population)• Mr Egidio and his family hid in nearby mountains in 1976


Monday: 28/06/04

Present:• Dr Warwick Crowe• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Dr Grey Coupland• Alex Wyatt• Halinka Lamparski

9 Appendices


ALL samples were taken from drains running through SAMÉ; for small scale analyses.

Location: R9• Time: 10:07am• Samples taken from drain next to major intersection of roads & ditches close to bottom of

town (before it dries out).• Physical measurements taken in slower deeper turbid/rubbishfilled catchment at end of

drain, due to lack of deep water (required for Yeokal) in faster flowing drain water.• GPS: S 09º 0.584’

E 125º 38.887’• Sunny & windy• Clear, fast flowing water in open concrete drain running along side of road• Overgrown, weeds & rubbish• Intersection of many people, animals, vehicles and houses


Location: R10• Time: 10:32am• Samples taken from open concrete drain (upstream of R9) near centre of Samé• GPS: 09º 0.284’

125º 38.942’• Clear, fastflowing water; no visible rubbish in water due to speed• Some moss & weeds growing in ditch• Site across from petrol station• Mechanics workshop ~20m up road & on same side of drain site• Buildings/shops on both sides of road & drain (running down one side of main road)• Many people, animals, grass/weeds & rubbish on either side of drain


9 Appendices


Location: R11• Time: 11:00am• Samples taken from open concrete drain just upstream of centre of Samé• GPS: S 08º 59.893’

E 125º 38.803’• Partially overcast• Clear water (visibility to bottom of drain) > visible change in water colour and flow as it

becomes almost completely turbid, containing plastics and rubbish (thongs) at muchhigher flow rate > after few minutes water becomes clear again and flow decreases =RELEASE OF RUBBISH

• Did not see change again, perhaps periodic?• Next to church and nearby primary school; across from dilapidated buildings• Chickens, ducks and people wandering nearby• Rubbish, leaf litter & weeds near & in drain• Site shaded by large tree


Location: R12• Time: ~11:20am• Samples from open concrete drain further upstream of town, closer to mountains• GPS: S 08º 59.550’

E 125º 38.092’• Mostly overcast > partially sunny• Clear water running in open drain• Not as many people living nearby, compared to R9R11• Water running into large sets of unused fish ponds next to drain (set up by Indonesians)• Moss/algae growth on sides & bottom of ditch• Weeds/grasses on both sides of ditch site, no trees overhead• Water passing from drain alongside & under main road into mountains


9 Appendices


Tuesday: 29/06/04

Present:• Dr Warwick Crowe• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Alex Wyatt• Halinka Lamparski

Location: R4• Time: 2:45pm• Physical measurements taken from small creek, next to end of 4WD track, part of ~150m

wide sandy/fine gravel river bed (Mota Caraulun) ~1km from river mouth• Water samples from this site were collected on Saturday (26/06/04)• GPS: S 09º 10.568’

E 125º 41.359’• Sunny• Clear water, slow flowing; compared to turbid, faster flowing water in various intersecting

creeks downstream of site, until river mouth (i.e. sample not representative of river at thisgeneral location)

• Yellow algae growing in many places nearby• Gravel/rocks in and around sample site• Very few people/animals (however some housing/living areas further back from track &

people noted along river; some animal faeces next to site)• Casuarina trees & bush nearby• Mountains in back ground (some kms away), water flowing towards coast, habitat of

crocodiles!

Source: Alex Wyatt 2004.

Saturday: 03/07/04

Present:• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Halinka Lamparski

Location: R13• Time: 9:45am• Samples & measurements taken in Mota Caraulun ~100m south of junction with Mota

Sui.• NOTE: At junction it is obvious (see my photos) that Mota Sui is much more turbid i.e. no

visibility, in comparison to Mota Caraulun, that is only partially turbid. At R13 and in

9 Appendices


water past junction, it is still partially visible indicating that Mota Caraulun waterdominates.

• GPS: S 09º 04.002’E 125º 41.755’

• Partially overcast• Partial visibility/turbidity of fast flowing water in wide rocky/gravel river• River water ~1/2 width of river bed when full (in wet season)• Casuarina trees lining far side of river bed• ~34m high banks of flaky white/grey/pink rock (Carbonaceous mudstone (pers comm Dr

Warwick Crowe) = HCl acid test; rapid bubbling in contact with stone)• Very few people (no nearby living areas) & animals obvious in the vicinity, only one

person seen riding horse with dogs crossing river


Location: R14• Time: 10:30am• Samples taken in Mota Caraulun ~100m north of junction with Mota Sui• GPS: S 09º 03.724’

E 125º 41.740• Mostly sunny, light breeze• Water had partial visibility/turbidity• ~10m dry rocky/gravel bed on near side of river, then 16.5m width of water (in which

sampling occurred), then 5m sand barrier, then another ~10m width section of water• ~10m Casuarinas on both sides of river bed (not overhanging)• Some green & brown algae on nearby rocks, in shallow/partially exposed bank region• Evidence of people washing clothes (empty powder packets nearby)• Closest villages ~0.51km from river bank (track leading from villages to river)

Location: R15• Time: 11:16am• Samples taken from Mota Sui, ~100m north of junction with Mota Caraulun• GPS: S 09º 03.861’

E 125º 41.926’• Mostly sunny, light breeze• Completely turbid water in wide rocky/gravel river bed (~50m wide)• Our samples taken in water section of 13m width; 10m dry bed on side & 30m on other

side• Casuarina trees on both sides of bed (no overhanging)• What appears to be limestone/mudstone cliffs a short distance downstream & further

upstream of sample site, ~24m high (perhaps contributing to turbidity of this river?)

9 Appendices


• Tiny amount of green algae on rocks in shallows, but only in one place• 1 lone fisherman, some horse faeces & tracks; in general very little signs of human/animal

activity

Location: R16• Time: 1:15pm• Samples taken from rocky creek joining the Mota Caraulun (~0.51km north of junction)• GPS: S 09º 02.644’

E 125º 40.090’• Completely overcast, stuffy & hot• Clear, slow flowing water, generally flowing amongst large boulders & rocks vertically

down at angle of ~30º• No overhanging trees, shrubs on high banks (~13m) either side of the creek• Green/grey colour (moss or algae?) on rocks in shallow waters• People seen washing further upstream of creek• Stream passes under bridge on main Samé/Betano road ~500m upstream of site• Large brown snake & little fish observed in water

Wednesday: 07/07/04

Present:• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Halinka Lamparski

Location: R17• Time: 3:05pm• GPS: S 08º 55.955’

E 125º 37.880’• Light rain, completely overcast• Located next to bridge off main road to Dili from Samé, crossing Mota Caraulun• Few villages in the vicinity, surrounded by moutains but many people walking to a

meeting point for local markets.• Small gravel river with slow flow• Moss on concrete bridge wall nearby• Short reeds and bushes, but not overhanging, expect to get most daylight.


E 125º 36.569’• Still completely overcast but not raining• Almost completely turbid, grey/brown water flowing down fast waterfall into mini pool.• Height of water fall approx. 10m.• Surrounded by reeds, and short casuarina trees, not overhanging.• Lage mossy rocks in water• Higher in the mountains than R17.• Village located downstream• Very few people (no housing nearby), some people and cars on the road and a couple of

animals.

9 Appendices


• One one side of site, banks of mud & small stones located ~45m high.


E 125º 41.416’• Overcast and light rain• Half clear, wide rocky/gravel section of Mota Caraulun• ~10m of dry river bed each side of flowing water• Located out of mountains in lower lands.• Casuarina, banana and cocount trees on each side• Animal faeces next to water• Some green algae on shallow rocks• Nearest house ~500m away.

Thursday: 08/07/04

Present:• Dr Warwick Crowe• Octávio do R.M. da Piedade (Ele, Dili Polytech student)• Gaspar da Costa da Jesus (Department of Energy and Mineral Resources)• Halinka Lamparski


E 125º 38.938’• Sample taken from tap water at back of house, from top of a concrete wall.• Overcast• Green moss growing on wall where tap is located.• Water travels down asbestos pipe.• Pipes carrying tap water appear to be made from steel, leaking.• ~25m steel piping before water enters black plastic piping.• YEOCAL measurements taken in black plastic bucket.


E 125º 41.545’• Completely overcast• Located in Mota caraulun, south of major junction before river hits coast• Tall Casuarinas on banks• Sediment loaded/muddy grey water, no visibility in majority of river water• Children nearby, boy on horse, access to village ~200m from river bed.• Green algae on rocks at eadge of river.


E 125º 41.534’

9 Appendices


• Overcast, cool• Located ~12km north of bridge before intersection on Mota Caraulun• Completely grey rocky/gravel water, green algae present• Fast flowing, wide, in river bed ~50m wide.• Mango, casuarina trees and bushes on either side.• Some river gravel/mudstone banks ~23m high, further upstream of river. Source of

muddiness?

Friday: 09/07/04

Present:• Jamie (Department of Energy and Mineral Resources)• Halinka Lamparski

Location: R23• Time: 9:43am• GPS: S 09º 10.726’

E 125º 41.300’• Completely clear sunny day• Returned to river mouth, to do replicates across ~150m river bed, ~1km from coast• Cloudy, grey water, slow flowing and completely turbid, rocky/gravel creek within bed

~50m from far side of bed• Tall casuarinas and palms on far side• Crocodiles said to live here.

Location: R24• Time: 10:05am• GPS: S 09º 10.769’

E 125º 41.288’• Still clear and sunny• Right next to edge of tropical forest• Water is grey, completely turbid, fast flowing• Some small amounts of green algae.• Appears to be the major input to the river mouth• ~80m from other side of rocky, gravel bed• ~40m from R23

9 Appendices


APPENDIX F: LIST OF EQUIPMENT USED DURING THE FIELD TRIP IN TIMORLESTE

General EquipmentPhysicalAnalyses

What How Much What How Much1 Sample 1 Day 8 Days 1 Sample 1 Day 8 Days

Waders 2 Yeokal 1Notebooks 1 Secchi Disks 2

Calculator 1ConductivityMeters 2

Scissors 1 pH paperLabel Tape 3 Rolls pH meters 1Masking Tape 1 Roll Timer 1Marker Pens (water proof) 4 Measuring Tape 1120ml Bottles 400Aluminium Foil 1 big roll WASTEForceps 2 setsGlad Wrap 1 rollCamera 1 manualGPS 1BatteriesGloves 3 SetsMeasuring Cylinder 250ml

ReferencesPhotocopies: Yeo/FlManuals

4

9 Appendices


Biological Analyses Chemical AnalysesWhat How Much What How Much

1 Sample 1 Day 8 Days 1 Sample 1 Day 8 DaysChlorophyll a Phosphate

Fluoro tubes/globes 2 SpareAquaspex Quantofix Test Strips(100) 1

1packet

Fluorometer 1Fluorometer leads: carbattery 1 set

Nitrate

Parafilm 1 rollITS Test Strips: NO3/NO2/N (50)

12bottles

13mm Glass tubes 400Tube Racks 2 Ammonium

Miniesky for Tube Rack 1Aquaspex Quantofix Test Strips(100) 1

1packet

Hand Pumps 1Plastic filter manifold (50mL) 1 Copper

50ml Syringes 2ITS Test Strips: Cu+1/Cu+2 (30)

11packet

Whatman 25mm GF/F filters 4 55 440Aluminium Foil 1 600ml ~5L Iron

90% Ethanol8ml + 4ml (error, washing) =

12mlITS Test Strips: Fe+2/Fe+3 (30)

13packets

10ml Syringes 4 75ml 600ml1M HCl Acid 3 drops = 1.5mlPasteur Pipette 3Forceps 2

WASTEAcetone/Ethanol waste JarsGlass waste containerGeneral waste container

9 Appendices


Documents

Assessing€Water€Quality€in€Developing … · 2011. 5. 27. · Assessing€Water€Quality€in€Developing€Countries:€A€Case€Study€in€TimorLeste ii From€TimorLeste: