PUGA-AN RIVER WATER QUALITY ASSESSMENT USING MACROINVERTEBRATES AND SELECTED PHYSICO-CHEMICAL CHARACTERISTICS AS INDICATORS

PU G A - A N RI V E R WA T E R Q U A L I T Y AS S E S S M E N T US I N G
MACROINVERTEBRATES AND SELECTED PHYSICO-CHEMICAL
CHARACTERISTICS AS INDICATORS
MARGIE G. APOSTOL
BACHELOR OF SCIENCE IN ENVIRONMENTAL SCIENCE
College of Forestry and Environmental Studies
Mindanao State University
Marawi City
May 2016

PU G A - A N RI V E R WA T E R Q U A L I T Y AS S E S S M E N T US I N G
MACROINVERTEBRATES AND SELECTED PHYSICO-CHEMICAL
CHARACTERISTICS AS INDICATORS
MARGIE G. APOSTOL
AN UNDERGRADUATE THESIS SUBMITTED TO THE COLLEGE OF
FORESTRY AND ENVIRONMENTAL STUDIES MINDANAO
STATE UNIVERSITY-MARAWI CITY IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
BACHELOR OF SCIENCE IN ENVIRONMENTAL SCIENCE
May 2016

BIOGRAPHICAL SKETCH
“Four steps to achievement. Plan purposefully. Prepare prayerfully. Proceed
positively. Pursue persistently.”
The author greatly believes that nothing is impossible with dauntless courage and
boundless determination. She was born on the dawn of July 18, 1995 at Tagum City,
Davao del Norte. Being the fourth child among the six children of Rufino B. Apostol and
Margarita G. Apostol, she never failed to continue her pursuit of wisdom. She finished
her elementary endeavour on 2008 at Rizal Elementary School I (RES I) and finished her
secondary education at Tagum City National Comprehensive High School (TCNCHS) in
the year 2012, as class Valedictorian on both. She decided to continue her tertiary
education in Mindanao State University-Main Campus in 2012 after missing the entrance
exam on University of the Philippines (UP). She took the degree of Bachelor of Science
in Environmental Science after getting inspired by Kuya Kim’s documentary film and
with the hope of discovering someday a species on her own. She was a former appointed
Editor-in-Chief of their college publication, The Conservator and had been a
representative of EnvironECS during her sophomore year. She is also a member of the
semi-academic organization Student League for Academic Advancement and Progress
(SLAAP). Being academically competitive, she had been a dean’s lister twice in her
college life. Lastly, her interest is pinned on environmentally-related activities such as
projects on tree-planting and seminars with environmental advocacies.

ACKNOWLEDGMENT
The author is forever indebted and thankful to everybody who inspired her a lot in
her colorful journey on her academic life.
Above all, to the Divine Providence who showed her that life is always beautiful
and that all struggles can always be resolved no matter what.
To her Mama Inday and Papa Boy, who made both ends meet for financial
support and the unfailing encouragement, affection, care and understanding they’ve
shared to the life of the author.
To her Lola Lily, Lolo Tony, Uncle Gody, Uncle Yoyong, Auntie Jinna, and
Auntie Lani for their sweet words of upliftment and financial assistance offered.
To Kuya Gerald, Ate Genevieve, Ate Glenna, Meryl and Boboy, for their
brotherly and sisterly love and comforting act most of the time when the author is
downhearted.
To Prof. Nelieta B. Arnejo-Bedoya, Chairman of the Advisory Committee, for her
endless support on the whole duration of the study and as a second-mother to the author
who was warmly welcomed in her home to have countless overnights for the study’s
success.
To Prof. Danilo C. Mero and Prof. Annabelle G. Villarino, members of the
Advisory Committee, for their time and knowledge on the statistics and identification of
the species.
To Marella and Shaira, who never forget to cheer up and remain as friends to the
author through thick and thin.

To Orlan and Sanry who lent their helping hands throughout the conduct of the
study and the brotherly kindness they had extended.
To the junior Environmental Science students: Honeybee, Eric, Mujah, Shady,
Mahdie, and Melchie who patiently assisted her during the conduct of the study.
To the Barangay Puga-an for their permission for the study to be conducted on
their barangay and to those residents who welcomed and guided the author to the study
area.
To the EnvironECS family who motivated her to continue everything behind all
those stress.
To Sir Bong and Sir Mioux, both Romeos who acted as the author’s second father
in MSU.
To Sir Alicante who helped her with the study materials needed and gave her
words of wisdom.
To the Hypers family: Nak Ludy and Nak Judea, Arnele, Antonette, Alexander,
Ate Amelyn, Ate Jam, Roirose, Habagat, Marjorie, Gwean, Adelyn, and Bhesh Russel for
the memorable experiences shared together throughout the four-year battle in college.
To her mommy Jinky, being a good roommate and understanding her every time
she throws tantrums out of frustration.
To Fayd, being a good buddy and a special friend to the author and in fulfilling
her pleadings for the study’s success and teaching her pacified ways to resolve problems.
To Kuya Zob, motivating the author to move forward behind all those exhaustion
felt.

To her high school mentor, Ma’am Alma Mercado, who unleashed her passion for
literary arts and taught her how to be firm to all those trials that come.
To SLAAP, which developed her confidence level to the level it is today.
To Ate Zash, Gigi, Diday, Mina, Lang2, Love2, Ate Rose and Ren2, new found
friends of the author who shared their joy and laughter behind all those stress received.
To Chap Mark, who did a final scrutiny and touch to the paper even to the last
minute.
And to those individuals that the author may have failed to mention but in one
way or another supported, helped, motivated, inspired and pushed her to get through with
the university life.
Mujie

To the Almighty above, to my dearest
family and friends, for the great
pursuit of my wildest dreams and for
the love of nature.
- MUJIE

TABLE OF CONTENTS
Preliminary Pages
Title page i
Approval Sheet ii
Biographical Sketch iii
Acknowledgment iv
Dedication ` vii
Table of Contents viii
List of Tables xii
List of Figures xiv
List of Appendices xvii
Abstract xviii
CHAPTER PAGE
I INTRODUCTION 1
Background of the Study 1
Statement of the Problem 2
Objectives of the Study 3
Research Hypothesis 3
Significance of the Study 5
Scope and Limitations of the Study 5
Conceptual Framework 7
Operational Definitions of Terms 9
II REVIEW OF RELATED LITERATURE 11
Water Pollution 11
Pollution Causes and Effects 12
Water Quality Assessment 14

Hydrodynamic Features 14
Physical and Chemical Features 15
Biological Characteristics 16
Macroinvertebrates 18
Physico-chemical Parameters Affecting Macroinvertebrates 21
Puga-an River Water Quality Assessments 26
III METHODOLOGY 27
Locale and Subject of the Study 27
Selection of the Study Site 29
Experimental Design and Layout 30
Experimental Materials 31
Methods of Data Collection 32
Macroinvertebrates Survey 32
Physico-Chemical Parameters Water Sampling 34
Key Informants Survey 35
Data Analysis 35
Water Quality Index 35
EPT Index 36
Shannon-Weiner Diversity Index 36
Statistical Analysis 36
IV RESULTS AND DISCUSSION 38
Physical Characteristics of the River 38
Stream Velocity 38

Depth 40
Temperature 41
Total Suspended Solids 43
Chemical Parameters 44
pH 44
Dissolved Oxygen 46
Biological Oxygen Demand 48
Nitrate 50
Test of Significant Difference on the Physico-Chemical 53
Characteristics of Puga-an River
Presence of Macroinvertebrates 54
Test on Independence on the Presence of Macroinvertebrates 65
on the Three Sampling Sites
Biodiversity Index 66
Test of Significant Differences on Macroinvertebrates Diversity 69
and Physico-Chemical Characteristics of Puga-an River
Water Quality of Puga-an River 71
EPT Index 71
Water Quality Index 74
Test of Significant Difference on Water Quality 78
Test of Significant Difference on Water Quality and 79
Macroinvertebrates Diversity of Puga-an River

V SUMMARY, CONCLUSION AND RECOMMENDATIONS 81
Summary 81
Conclusion 83
Recommendations 84
LITERATURE CITED 86
APPENDICES 90

LIST OF TABLES
TABLE PAGE
1 Experimental Layout of the Study 30
2 Water Quality Index 35
3 One-way ANOVA of the Stream Velocity of the Three Sampling Sites 39
4 Tukey’s Pairwise Comparisons of Stream Velocity 39
in the Three Sampling Sites
5 One-way ANOVA of the Depth of the Three Sites 41
6 Tukey’s Pairwise Comparisons of Depth in the Three Sites 41
7 One-way ANOVA of the Temperature of the Three Sites 42
8 Tukey’s Pairwise Comparisons of Temperature in the Three Sites 42
9 One-way ANOVA of the TSS of the Three Sampling Areas 44
10 Tukey’s Pairwise Comparisons of TSS in the Three Sampling Areas 44
11 One-way ANOVA of the pH of the Three Sampling Sites 45
12 Tukey’s Pairwise Comparisons of pH in the Three Sampling Sites 46
13 One-way ANOVA of the DO of the Three Sampling Sites 47
14 Tukey’s Pairwise Comparisons of DO in the Three Sampling Sites 47
15 One-way ANOVA of the BOD of the Three Sampling Sites 49
16 Tukey’s Pairwise Comparisons of BOD in the Three Sampling Sites 50
17 One-way ANOVA of the NO3 of the Three Sampling Sites 51
18 Tukey’s Pairwise Comparisons of NO3 in the Three Sampling Sites 51
19 ANOVA of Different Physico-Chemical Characteristics of 53
Puga-an River

20 Tukey’s Pairwise Comparisons of Different Physico-Chemical 54
21 Macroinvertebrates Present in Upstream (Purok 4: Guiaon) 55
22 Macroinvertebrates Present in Midstream (Purok 2: Market Area) 60
23 Macroinvertebrates Present in Downstream 61
(Purok 1-D : Submerged Bridge)
24 Chi2
of the Upstream and Midstream Sites 65
25 Chi2
of the Midstream and Downstream Sites 66
26 Chi2
of the Upstream and Downstream Sites 66
27 Shannon-Weiner Diversity Index of the Three Sampling Sites 67
28 One-way ANOVA of the Shannon-Weiner Index of the 68
Three Sampling Sites
29 Tukey’s Pairwise Comparisons of Shannon-Weiner Index 68
of the Three Sampling Sites
30 ANOVA Between Physico-Chemical Characteristics and 70
Diversity of Puga-an River
31 Tukey’s Pairwise Comparisons of Different Physico-Chemical 70
Characteristics and Macroinvertebrates Diversity of Puga-an River
32 EPT Index of the Upstream, Midstream and 72
Downstream of Puga-an River
33 Water Quality Index of the Three (3) Replicates in 75
Upstream (Purok 4: Guiaon)
34 Water Quality Index of the Three (3) Replicates in 76
Midstream (Purok 2: Market Area)
35 Water Quality Index of the Three (3) Replicates 77
in Downstream (Purok 1-D Submerged Bridge)
36 ANOVA Between Water Quality and Physico-Chemical 78

37 Tukey’s Pairwise Comparisons ANOVA Between Water Quality 79
38 ANOVA Between Water Quality and Macroinvertebrates Diversity 80
of Puga-an River
39 Tukey’s Pairwise Comparisons Between Water Quality 80
and Macroinvertebrates Diversity of Puga-an River

LIST OF FIGURES
FIGURE PAGE
1 Conceptual Model of the Study 8
2 Location Map of the Study Area 28
3 River Flow of the Three Streams 30
4 Kick net 32
5 Dip Net 33
6 Drift Net 33
7 On-site Identification of Macroinvertebrates 34
8 Physico-chemical Parameters Water Sampling 36
9 Stream Velocity of the Three Sampling Sites 38
10 Water Depth of the Three Sampling Sites 40
11 Temperature of the Three Sampling Sites 41
12 TSS Concentration of the Three Sampling Sites 43
13 pH of the Three Sampling Sites 45
14 DO of the Three Sampling Sites 47
15 BOD of the Three Sampling Sites 49
16 NO3 of the Three Sampling Sites 51
17 Pagoda snail from the Order Caenogastropoda 56
18 Freshwater limpet from the Order Patellogastropoda 56
19 Gilled Snail From the Order Neotaenioglossa 57

20 Stonefly from the Order Plecoptera 57
21 Caddisflies in Leaf Cases From the Order Trichoptera 58
22 Dragonfly Nymph From the Order Odonata 59
23 Tubifex Worm From the Order Haplotaxida 59
24 Dobsonfly From the Order Megaloptera 60
25 Leech From the Order Arhynchobdellida 61
26 Pouch Snails From the Order Pulmonata 62
27 Horsefly Larvae From the Order Diptera 63
28 Cranefly Larvae From the Order Diptera 63
29 Frequency Distribution of Different Macroinvertebrates Groups 64
Across the Puga-an River

LIST OF APPENDICES
APPENDIX PAGE
1 Profile of Barangay Puga-an 91
2 Map of the Three Replicates in the Upstream Site 92
3 Map of the Three Replicates in the Midstream Site 93
4 Map of the Three Replicates in the Downstream Site 94
5 Demography of Barangay Puga-an 95
6 Puga-an River Stretch (adapted from Bedoya 2008) 96
7 Photo Documentation of the Sampling Sites 97
8 Materials Used in the Study 99
9 Analyses Procedures of MSU-Naawan Research Division: 100
Chemistry Laboratory
10 Analyses Procedures of MSU- Main Campus College of 102
Natural Sciences and Mathematics: Chemistry Department
11 Profile of Key Informants 103
12 Questionnaire for Key Informants 106
13 Laboratory Analysis and Measurement Results 107
14 Treatment Means for ANOVA 110
15 Diversity Statistical Analysis 114

ABSTRACT
APOSTOL, MARGIE G. Mindanao State University- Main Campus, Marawi City. May
2016. Puga-an River Water Quality Assessment Using Macroinvertebrates and
Selected Physico-Chemical Characteristics as Indicators.
Thesis Adviser: Prof. Nelieta B. Arnejo-Bedoya
The water quality assessment was conducted in Puga-an River to find out some of
the physico-chemical characteristics, different macroinvertebrates and their diversity and
to determine the water quality of the three sampling sites (upstream Purok 4- Guiaon,
midstream Purok 2- market area and downstream Purok 1-D -submerged bridge) of
Barangay Puga-an in relation to surrounding land-uses through key-informant interviews.
This was done using the kick net, dip net and drift net for macroinvertebrates collection.
Water quality indices used were the Water Quality Index by Kanjanavanit and Tilling and
EPT Index. The diversity index of macroinvertebrates was assessed through Shannon-
Weiner diversity index. The different physico-chemical characteristics of the river
measured were the stream velocity, depth, temperature, TSS, pH, DO, BOD, and NO3.
Test of significant differences within physico-chemical characteristics, between physico-
chemical characteristics and macroinvertebrates diversity, between physico-chemical
characteristics and water quality, and between water quality and macroinvertebrates
diversity were determined through ANOVA and Tukey’s Test. Test of independence on
presence of macroinvertebrates between sampling sites was done through Chi2
.
Results showed that the upstream site has 10 different orders of
macroinvertebrates. The midstream has 5 different orders of macroinvertebrates and the
downstream has 7 macroinvertebrates orders. The upstream has a rather clean type of
water. The midstream and downstream had a rather dirty water. Only the upstream has a
significant value for EPT index. The midstream and downstream EPT indices has no
values since EPT orders were not observed on the said sites. The Shannon-Wiener index
of the upstream is the highest compared to midstream and downstream. Significant
differences were noted on the different physico-chemical characteristics of the river,
between physico-chemical characteristics and macroinvertebrates diversity, between
physico-chemical characteristics and water quality, and between water quality and
macroinvertebrates diversity. Presence of macroinvertebrates between sampling sites
were highly independent from each other.

CHAPTER I
INTRODUCTION
Background of the Study
Bodies of water like rivers, streams, lakes, seas and rivers are important to
humanity. Rivers especially play an important part in the livelihood and development of
the humanity. Most industries and cities are built on nearby rivers as it would be a means
of transportation and as an outlet for wastes discharge even during early times until
present (McKinney et.al 2003).
In the advent of modernization, anthropogenic activities are rampant nearby a
riverine ecosystem. As such, agricultural practices are concentrated near rivers. Adverse
impacts could result to alteration of flow patterns of the river, swelled sediment pollution
and varying water temperature that in turn deteriorate water quality and affecting aquatic
ecosystems, including macroinvertebrates (McKinney et.al 2003).
Different strategies to assess such impacts are available but have disadvantages
that could yield unreliable and insufficient findings. On the other hand, biological
monitoring or also called biomonitoring offers the better assessment technique in
determining water quality of a river. Biomonitoring checks-up on aquatic insects and
other invertebrates (Heist 2015). In many water quality assessments, employment of
benthic macroinvertebrates has long been practiced. Benthic macroinvertebrates are
bottom-dwelling organisms that include variety of aquatic insects, crayfish, clams, snails
and worms (Oleson 2013). Their sensitivity to physical and chemical changes in their
habitat, various pollution tolerances, limited mobility and accessibility for collection

made them suitable indicators of water pollution (North Dakota Department of Health
Surface Water 2005).
Using physico-chemical characteristics in water quality assessments is insufficient
when habitat degradation via channelization or sedimentation from non-point pollution
occurs (Marst 2015). Thus, water sampling alone could be insufficient (Herbst 2005).
However, some physico-chemical characteristics such as temperature, Total Suspended
Solids (TSS), pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and
Nitrates concentration are necessary to strengthen assessment of water quality as these
could be compared to standard values assigned for river waters (Patil et.al 2012). All of
these, together with other parameters such as stream velocity and depth were used in the
study.
Statement of the Problem
The macroinvertebrates as biological indicators measured the water
quality of the said river. The declining population of such species signals as river water
pollution which could impact heavily on the environment. Thus, there was necessity in
assessing the water quality of Puga-an River.
In the context of the above-mentioned problem, this study seeks to answer the
following questions:
1. What are some of the physico-chemical characteristics of the river?
2. What are the different macroinvertebrates and their diversity?
3. What is the water quality along Puga-an River?

4. Is there any significant difference on the physico-chemical characteristics of the
river, the diversity of the macroinvertebrates and the water quality considering sampling
sites as well as between physico-chemical and macroinvertebrates diversity; between
physico-chemical characteristics and water quality; and between water quality and
macroinvertebrates diversity?
Objectives of the Study
The purpose of the study was to determine the water quality of Puga-an River
using macroinvertebrates as water quality indicators. Eventually, this study was aimed to
achieve the following objectives:
1. To find out some of the physico-chemical characteristics of the river.
2. To determine the different macroinvertebrates species and their diversity.
3. To assess the water quality of the Puga-an River.
4. To evaluate if there is significant differences on the physico-chemical
characteristics of the river, the diversity of the macroinvertebrates and the water quality
considering sampling sites as well as between physico-chemical and macroinvertebrates
diversity; between physico-chemical characteristics and water quality; and between water
quality and macroinvertebrates diversity.
Research Hypothesis
HO1: There is no significant difference on the physico-chemical characteristics
in the different sampling sites of the river.

HO2: There is no significant difference on the presence of different
macroinvertebrates groups in the different sampling sites of barangay Puga-an.
HO3: There is no significant difference on the diversity of macroinvertebrates in
the different sampling sites of Barangay Puga-an.
HO4: There is no significant difference on the diversity of macroinvertebrates
and the physico-chemical characteristics in the different sampling sites of Barangay
Puga-an.
HO5: There is no significant difference on the water quality and the physico-
chemical characteristics in the different sampling sites of Barangay Puga-an.
HO6: There is no significant difference on the water quality and the diversity of
macroinvertebrates in the different sampling sites of Barangay Puga-an.
Ha1: There is significant difference on the physico-chemical characteristics in
the different sampling sites of the river.
Ha2: There is significant difference on the presence of different
macroinvertebrates groups in the different sampling sites of barangay Puga-an.
Ha3: There is significant difference on the diversity of macroinvertebrates in the
different sampling sites of Barangay Puga-an.
Ha4: There is significant difference on the diversity of macroinvertebrates and
the physico-chemical characteristics in the different sampling sites of Barangay Puga-an.
Ha5: There is significant difference on the water quality and the physico-
chemical characteristics in the different sampling sites of Barangay Puga-an.
Ha6: There is significant difference on the water quality and the diversity of
macroinvertebrates in the different sampling sites of Barangay Puga-an.

Significance of the Study
The results of this study will be beneficial to the river itself. This would allow
collection of current biodiversity information on macroinvertebrates found within the
river. Determination of the river’s physico-chemical characteristics would facilitate
evaluation if the current human activities surrounding the river have impacted its water
quality. Also, as there had been studies conducted by many researchers about the water
quality of the river, this study would compare results of present assessment with the
previous studies and determine if there is any change in the water quality over time.
Upon subsequent findings, a picture of environmental conditions can be created,
allowing for proper management of waters, track progress of habitat recovery, detect
aquatic life values vulnerability at such locations and eventually facilitate environmental
protection for the sake of Barangay Puga-an.
Furthermore, this study would pave way for potential species discoveries and may
lead to additional scientific research seeking for future data of macroinvertebrates
monitoring along the river.
Scope and Limitation of the Study
This study was conducted at Purok 4- Guiaon, Purok 2 (market area) and Purok 1-
D (submerged bridge) as sampling sites of Puga-an River of Iligan City, Lanao del Norte.
This focused on the water quality assessment of the said river primarily using
macroinvertebrates as indicators. Macroinvertebrates were collected using kick net on the
riffle areas, dip net on the pool areas, and drift net for the running surface waters.

To determine the present water quality using macroinvertebrates, the water
quality index using the reference “A Guide to Freshwater Invertebrates of Ponds and
Streams in Thailand” adapted by Kanjanavanit and Tilling and the Ephemeroptera,
Plecoptera and Trichoptera (EPT) index, were used. The Shannon diversity index was
used in determining the diversity of macroinvertebrates species.
Physical characteristics of the river were focused only on stream velocity, depth,
temperature, and TSS and the chemical parameters of the river such as pH, DO, BOD and
NO3 were also analyzed.

Conceptual Framework
Water pollution caused by anthropogenic activities such as agricultural and urban
land-resource uses may have altered both the physical and chemical properties of a river
(Gest 2011). As in the case of Puga-an River, residential areas proliferate adjacent to the
water course. Most of the residents utilize the river waters as washing area for laundry,
bathing, sometimes as wastes disposal area and quarrying activities with land use/
agricultural practices along its riparian portions. Due to these human activities along the
river, water quality deterioration is not impossible. Thus, the assessment of the river
water quality was necessary.
To effectively evaluate water quality, some physical characteristics and chemical
parameters were utilized. Stream velocity, depth, temperature and TSS were measured
for physical characteristics. The pH, DO, BOD, and NO3 were considered for chemical
parameters. Meanwhile, for biological monitoring the survey on macroinvertebrates as
water quality indicators was applied. The Water Quality Index and the EPT Index were
used to determine water quality.

Figure 1. The conceptual model of the study.
Water Pollution
Water Quality Deterioration
Anthropogenic Activities
Water Quality Assessment
Physical
Characteristics
(depth,
temperature,
stream
velocity, and
TSS)
Biological Parameter
Water Quality
Chemical
Parameters
(pH, DO,
BOD, and
NO3)

Operational Definition of Terms
Biological indicators- these are animals used as tools in this study to determine water
quality of the river due to their tolerance/intolerance to pollution
Biological monitoring- a technique in water quality analysis where living things are used
as indicators to determine severity of pollution, in this study they are macroinvertebrates
Dip net or D-frame net – instrument for macroinvertebrates sampling usually utilized
for deeper portions and large rivers
Drift net- an instrument for macroinvertebrates sampling used to catch drifting animals
in the surface waters
EPT index- used for evaluating the number of Ephemoptera, Plecoptera and Trichoptera
species to determine water quality
Intolerant macroinvertebrate species- these are macroinvertebrates that cannot tolerate
high water pollution levels and are usually sensitive to any changes in their living
environment
Kick net- a sampling instrument for macroinvertebrates used for rivers with depths less
than a meter
Macroinvertebrates- the subject of this study that were assessed based on population,
and are usually small invertebrates living the river ecosystems
Physico-chemical characteristics- in this study, these are some river characteristics used
to indicate water quality which includes: stream velocity, depth, temperature, TSS, pH,
DO, BOD, and NO3
Puga-an River- is the water body that was sampled in this study, located in Iligan City
and had been studied in previous years

Shannon-Weiner diversity index- in this study, a guide used in determining species
diversity and richness of the macroinvertebrates population
Tolerant macroinvertebrate species- these are macroinvertebrates that can tolerate high
water pollution levels
Total Number of Midges- these are the total count of the pollution-tolerant species of
macroinvertebrates from the family Chironomidae of the order Diptera used in
determining EPT index of the river
Water Quality Index- a table with an index for water quality and its corresponding
scores based on the reference “A Guide to Freshwater Invertebrates of Ponds and Streams
in Thailand” adapted by Kanjanavanit and Tilling

CHAPTER II
REVIEW OF RELATED LITERATURE
Water Pollution
Water pollution is caused by one or more substances that built up in the aquatic or
marine environment to the extent that they severely impact animals and people. Water
bodies can naturally detoxify contamination through time but the quantity of pollution
matters. This would in turn affect the health of plants, animals and humans who are
dependent on the water resource. However, effects could be direct or indirect (Woodford
2006). Aquatic environment pollution is simply an introduction by man, directly or
indirectly, of substances or energy which results in deleterious effects such as harm to
living resources, hazards to human health, hindrance to aquatic activities including
fishing, impairment of water quality with respect to its use in agricultural, industrial and
often economic activities, and reduction of amenities (Chapman 1996).
Surface waters are obviously affected by water pollution as physical features
could easily determine pollution. Water stored underground in aquifers is called
groundwater, and this resource could also be prone to contamination since leaching from
substances applied above ground could seep through soils. Groundwater feeds rivers and
supplies mostly the drinking water (Woodford 2006).
Pollution could occur in two different ways. First is the point-source pollution
where direct pollution activities is being done like the act of discharging wastewater
directly through rivers. The other one, being the nonpoint-source pollution, occurs as a
result from different scattered sources (Woodford 2006).

Pollution Causes and Effects
The water pollution has many causes thus, making it a difficult environmental
problem to solve. One of these causes is largely human-driven. Eventually, any human
activity could affect the water quality of the environment. Any chemicals released by
industrial, domestic or agricultural activities on air could end up in the atmosphere and
fall back to earth as rain, entering seas, rivers, and lakes, causing water pollution. This is
called atmospheric deposition (Woodford 2006).
With increasing population, disposing of sewage waste has also become a major
environmental problem. According to 2013 figures from World Health Organization,
40% of the world’s population does not have proper sanitation and little efforts were done
to improve the global sanitation. Sewage disposal may affect people’s environment and
may cause water-borne illnesses and diseases such as diarrhea. Sewage may be composed
of many kinds of chemicals including pharmaceutical drugs, paper, plastic and other
wastes. Viruses may also be present and if carried into the environment, these may cause
hepatitis, typhoid, and cholera from river and sea water (Woodford 2006). The bacteria
most commonly found in polluted water are coliforms excreted by humans. Surface
runoff and consequently non-point source pollution contributes significantly to high level
of pathogens in surface water bodies. Improperly designed rural sanitary facilities also
contribute to contamination of groundwater (FAO 2015).
Another root cause of water pollution is the excessive nutrient loading from
detergent powders, and fertilizers and pesticides used in agriculture which could result to
eutrophication. Phosphorus is an element found in these substances which is essential to
life but harmful in excessive amounts. A sign of too much phosphorus in water is the

growth of algae or plankton that may spread over large areas of oceans, lakes and rivers.
Too much algal growth or bloom could rapidly remove oxygen from water leading death
of other life forms (Woodford 2006). Other agricultural activities like tillage or plowing,
manure spreading, feedlots or animal corrals, irrigation, clear cutting, silviculture, and
aquaculture may have impacts like turbidity or sedimentation, eutrophication, surface
water and groundwater contamination. These impacts could eventually affect public
health and well-being. The most common diseases associated with contaminated
irrigation waters are cholera, typhoid, ascariasis, amoebiasis, giardiasis, and
enteroinvasive E. coli. (FAO 2015).
Even waste waters from drains out of laundry, bathing and dishwashing or more
commonly known as greywaters could contaminate water ecosystems. According to
World Health Organization, these greywaters are chemically composed of elements like
nitrogen and phosphorus. Heavy metals may also possibly accumulate as contaminants.
(Woodford 2006).
Other forms of pollution may include thermal pollution. This is driven by
wastewaters released by factories and power plants. The effects simply raise the water
temperature which may not be a livable condition for many narrow tolerant organisms.
This also reduces the oxygen dissolved in the water. Sedimentation is another problem of
water ecosystems. Due to construction activities proliferating adjacent to water bodies,
sediments and dusts may be added to the water courses (Woodford 2006).
Any activities that involved the water body may actually pollute it through time.
However, to be able to determine the pollution assessment must be done on whatever
water body is involved.

Water Quality Assessment
Water pollution seriously affects water quality. One of the basic human needs
include clean fresh water. It is not only a human commodity but also an important natural
resource. Protecting or improving water quality is a great concern to countries around the
world as it is continually degraded by nonpoint pollutant sources. Thus, deteriorating
water bodies should be carefully evaluated and treated (Kenny 2009).
Water quality assessment is the overall process of evaluation of the physical,
chemical and biological nature of water in relation to natural quality, human effects and
intended uses, particularly uses which may affect human health and the health of the
aquatic system itself (Chapman 1996).
Aquatic environment quality is the set of concentrations, speciations, and physical
partitions of inorganic or organic substances. It is also the composition and state of
aquatic biota in the water body. It also depicts the temporal and spatial variations due to
factors internal and external to the water body (Chapman 1996).
In assessing aquatic environment quality, there are several ways that can be
applied for lotic water bodies or commonly known as flowing waters like streams, and
for lentic water bodies or the still waters such as lakes. The different methods involve the
hydrology, physical and chemical (i.e., physico-chemical) properties and biological
components of the water bodies. (Kenny 2009; Chapman 1996).
Hydrodynamic Features
Freshwater bodies are interconnected, from the atmosphere to the sea, via the
hydrological cycle. This shows that water constitutes a continuum, with different stages
ranging from rainwater to marine salt waters. The inland freshwaters such as the rivers,

lakes or ground waters are closely interconnected and may influence each other directly,
or through intermediate stages. However, each type has distinct hydrodynamic properties
(Chapman 1996). This said, whatever happens in the river is technically connected to the
hydrological cycle that which could affect other water bodies.
Generally, rivers are distinguished by unidirectional current with a relatively high,
average flow velocity ranging from 0.1 to 1 m s-1. Depending on the climatic situation
and the drainage pattern, the river flow is highly variable in time. Driven by prevailing
currents and turbulence, thorough and continuous vertical mixing occurs on rivers. Over
considerable distances downstream of major confluences will only lateral mixing may
take place (Chapman 1996). Thus, downstream was also selected in this study.
Physical and Chemical Features
Each freshwater body has an individual pattern of physical and chemical
characteristics which are determined largely by the climatic, geomorphological and
geochemical conditions prevailing in the drainage basin and the underlying aquifer.
Summary characteristics, such as total dissolved solids, temperature, pH, turbidity,
alkalinity and hardness provide a general classification of water bodies of a similar nature.
Another vital feature of any water body is its oxygen content which affects the solubility
of metals and is vital for all forms of biological life. Thus, for chemical tests, Biological
Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO)
are also important to be evaluated (Chapman 1996; Patil et.al 2012).
However, parameters such as temperature, pH, DO, BOD, COD could be enough
to assess water quality. Temperature regulates chemical reactions, life growth and
reproduction of organisms and immunity. Low pH would indicate reduced photosynthetic

activity, assimilation of carbon dioxide and bicarbonates. DO is the most important
because it will give information on bacterial activity, photosynthesis, availability of
nutrients and stratification. On summer seasons, it is lower due to high microbial activity
and at the same time could also be higher due to high temperature and extreme sunlight.
The BOD determines the organic material contamination in water at mg/L measurements.
It is simply the DO required for biochemical decomposition of organic compounds and
oxidation of inorganic materials. This is usually conducted on a five-day period. Lastly,
COD is just the DO measurement that will cause chemical oxidation of organic material
in water (Patil et.al 2012).
Moreover, to get the idea about the quality of the water, it is necessary to compare
results of different physicochemical parameter values with standard values (Patil et.al
2012).
Physico-chemical parameters, which provide snapshots of the condition of a water
body, do not provide the integrative measure of overall health of a stream and can, at
times, inadequately identify impaired waters. Instead, biological measures provide an
integrated, comprehensive assessment of the health of a water body over time. These
biological indicators, also called biocriteria, use measures of the biological community
including lower trophic level organisms, such as algae or benthic macroinvertebrates, as
well as upper trophic level species, such as fish (Kenny 2009).
Biological characteristics
Assumptions were made that chemical sampling for water quality is enough to
assure biological populations and thus, biological monitoring is often overlooked. Water
quality assessments based exclusively on physicochemical parameters are often

inadequate. Non-point pollution occurs and results to habitat degradation from
channelization or sedimentation. The inherent variability of biological populations due to
habitat preferences and seasonality, in addition to taxonomic difficulties in identification,
has hampered the development of tools to assess water quality (Maret 1988).
Direct measures of the health of the fauna and flora in the waterway can be
determined by biological indicators. Commonly used biological indicators in freshwater
include various measures of macroinvertebrate or fish diversity, benthic algal growth and
benthic oxygen demand (Queensland Government 2015).
The development of biota (flora and fauna) in surface waters is governed by a
variety of environmental conditions which determine the selection of species as well as
the physiological performance of individual organisms. The primary production of
organic matter, in the form of phytoplankton and macrophytes, is most intensive in lakes
and reservoirs and usually more limited in rivers. The degradation of organic substances
and the associated bacterial production can be a long-term process which can be
important in groundwaters and deep lake waters which are not directly exposed to
sunlight (Chapman 1996).
Chemical quality of water bodies can only be determined by suitable analytical
methods, while the biological quality of a water body is a combination of qualitative and
quantitative characterization. Biological monitoring can generally be carried out at two
different levels: the response of individual species to changes in their environment or, the
response of biological communities to changes in their environment (Chapman 1996).
Biological quality, including the chemical analysis of biota, has a much longer
time dimension than the chemical quality of the water since biota can be affected by

chemical and or hydrological, events that may have lasted only a few days, some months
or even years before the monitoring was carried out (Chapman 1996).
Biological assessment of stream or river ecosystem health can be done through
macroinvertebrates sampling. Biotic communities easily respond to alterations in habitat
and water quality due to anthropogenic disturbance and those community responses are
integrated indicators of the state of the biotic and abiotic variables representing stream
health (Kenny et.al 2009).
Whatever biotic assemblages used, each have particular advantages in bio-
assessments. However, stream macroinvertebrates are typically used due to the simple
equipment needed to sample them and the comparative ease of the sample processing.
Also when it comes to mobility, macroinvertebrates are typically less mobile than fish,
thus providing a more localized assessment of their response to stream conditions (Kenny
et.al 2009).
Macroinvertebrates
Macroinvertebrates are animals without backbones and yet large enough to be
seen with the unaided eye. The benthic macroinvertebrates are the common inhabitants of
lakes and streams. They live at the bottom substrates as indicated by the term benthic
which means bottom-living (Rosenberg et.al 1993; Schumaker Chadde 2012).
Important ecological functions like decomposition, nutrient cycling, and roles in
aquatic food webs as both consumers and prey were provided by freshwater benthic
macroinvertebrates which include representatives of many insect orders, as well as
crustaceans, gastropods, bivalves and oligochaetes. However, insects are often the

dominant group of benthic macroinvertebrates in both absolute numbers and species
diversity, which is not surprising given that the juvenile stages of many terrestrial insects
are typically aquatic. The structure of macroinvertebrate communities depends on abiotic
and biotic factors that vary across spatial scales from regional to habitat specific (Kenny
et.al 2009).
The natural features of stream and terrestrial habitats can affect macroinvertebrate
assemblage structure. These features include the quality and quantity of food resources,
habitat quality such as the physical structure of the stream bed, flow regime such as the
frequency and intensity of storm-flow disturbance, water quality, biotic interactions, and
the condition of the riparian zone (Kenny et.al 2009).
Agricultural and urban land-uses greatly alter both the physical and the chemical
aspects of macroinvertebrate habitat, impacting the structure of macroinvertebrate
communities. Land-use change through a chain of indirect effects can lead to changes to
the macroinvertebrate assemblage in both taxa richness and relative abundance. These
relationships between macroinvertebrate communities and stream ecosystem conditions
make community structure a good indicator of overall stream health (Kenny et.al 2009).
The macroinvertebrate orders are the Ephemeroptera (Mayfly), Plecoptera
(Stonefly), Trichoptera (Caddisfly), Megaloptera (Dobsonfly / Hellgrammite), Coleoptera
(Aquatic Beetles), Diptera (True Flies), Odonata (Dragonfly & Damselfly), Pelecypoda
(Clams), Gastropoda (Snails) and Hemiptera (True Bugs) (Schumaker Chadde 2012).
The stream macroinvertebrates are categorized into three groups according to
pollution vulnerability. The Group 1 is where the pollution sensitive macroinvertebrates
belonged. They require higher DO, neutral pH and cold water. Under this group is where

mayflies, stoneflies, caddisflies belonged. The Group 2 is the somewhat pollution tolerant
macroinvertebrates such as the scuds, dragonflies and damselflies. Lastly, the Group 3 is
the pollution tolerant macroinvertebrates since they can tolerate low oxygen, lower and
higher pH and warmer water. This group includes aquatic worms and midge larva
(Schumaker Chadde 2012).
Measuring macroinvertebrate communities have different methods and yet mostly
are based on population and community ecological theory. Simple measures include
abundance and richness of assemblages or communities and are often used in assessments;
species-poor systems are generally assumed to have degraded water quality. Some taxa,
such as stoneflies (Plecoptera), are known to be sensitive to pollutants. They are often
considered an indicator of a healthy stream. The orders Ephemeroptera (mayflies),
Plecoptera (stoneflies), and Trichoptera (caddisflies) are grouped into the sensitive taxa,
the EPT or just simply the Ephemoptera, Plecoptera and Trichoptera orders. This
measures the proportion of individuals in and is also used as an indicator of a healthy
stream (Kenney et.al 2009).
On the other hand, the abundance or increased population of some
macroinvertebrates species may also imply pollution problems. Increased abundance of
certain mayflies especially the Caenidae family with protected abdominal gills and
hemoglobin-possessing bloodworms of the Chironomidae family also mean a disrupted
water quality (Cranston et.al 1996). Other taxa of pollution-tolerant species include water
striders, backswimmers and water bugs which get oxygen from the air and not on the
dissolved oxygen in the water. Also, the Midges so-called as aquatic worms, are

pollution-tolerant. These are from the Oligochaeta taxa where leeches and pouch snails
also belonged (Schumaker Chadde 2012).
Physico-Chemical Characteristics Affecting Macroinvertebrates
Macroinvertebrates’ lives are also influenced by their physico-chemical
environment that determines their distribution patterns at the same time.
Some authors noted that temperature, levels of oxygen, suspended sediment and
water chemistry, commonly affect the macroinvertebrates, as supported by many studies
conducted (Letort 2010).
Water temperature with regards to latitude, altitude, seasons, and relative distance
from the source influence life of macroinvertebrates. Some genera of macroinvertebrates
occur in varying altitude which has relation with temperature (Letort 2010).
In a study conducted in Tallgrass Prairie Stream North-Central Oklahoma, United
States of America involving the community structure and distribution patterns of aquatic
macroinvertebrates, annual species diversity values were high at all sampling sites
indicating good water quality. However, a general pattern emerges on the species
diversity at each site for each collection was observed. Results showed that the values
were lowest on in July and mostly increased following summer months. The low
diversity values in July may be due to occurrence of higher temperatures limiting some
species survival (Bass 1994).
Study conducted involving macroinvertebrate diversity was done in Karst Jadro
River, Croatia. Results showed that the highest taxa richness found were the mayflies
under order Ephemeroptera. It has also been found out that structure and abundance per
sample of the macroinvertebrates species change seasonally and depends upon the

sampling site (upstream, midcourse and downstream). For insects, a considerable
increase in abundance occurred during spring or summer (Rada and Puljas 2008). Thus, it
can be said that temperature somehow affects macroinvertebrates abundance.
Meanwhile, the dissolved oxygen concentrations are also fundamental for water
quality assessment. DO influence all chemical and biological processes within water
bodies. In relation to macroinvertebrates, the pollution-sensitive species require higher
DO levels (Chapman 1996; Schumaker Chadde 2012). This supports the monitoring and
assessment study of water health quality done in the Tajan River, Iran using physico-
chemical, fish and macroinvertebrates indices.
As one of the findings in the study, DO is the only physico-chemical parameter
that has a positive correlation with the abundance of the very sensitive and sensitive
macroinvertebrates species. The other physico-chemical parameters have negative
correlations with both groups. The study also implies that BOD level is low in correlation
to abundant level of pollution- sensitive macroinvertebrates (Aazami et.al 2015).
The physico-chemical parameter BOD was found to play an important role in the
diversity of macroinvertebrates as revealed in the study of physico-chemical parameters
and macroinvertebrates fauna of Ona River at Oluyole Estate, Ibadan, Nigeria. Results
showed that there is low diversity of pollution-sensitive macroinvertebrate present which
corresponds to high BOD level recorded to the selected sampling stations. This simply
implies that the water is polluted (Adjarho 2013).
The physical parameter suspended and colloidal matter (microscopic particles that
remain suspended in water and diffract light) can be anything that is suspended in the
water column ranging from sand, silt, clay, plankton, industrial wastes, sewage, lead, and

asbestos to bacteria and viruses. Some suspended matter occurs naturally and some is
produced by human activities. Aquatic organisms are particularly susceptible to the
effects of increased sediments and turbidity. Many fish need clear water to spot their prey.
Macroinvertebrates, fish eggs, and larvae require oxygen-rich water circulating through
clean gravel beds to survive (Project Wet 2011). The type and concentration of suspended
matter controls the turbidity and transparency of the water (Chapman 1996).
Furthermore, other parameters affecting macroinvertebrates’ lives include water
chemistry. This is where the pH, nitrogen and phosphorus concentration parameters of
water are considered.
Generally, natural waters have a pH of between 5 and 9 and most aquatic
organisms survive in waters within this range. With the exception of some bacteria and
microbes, if pH goes higher or lower than this range, aquatic life is likely to perish. Other
water quality problems can stem from high or low pH levels. Water with low pH
increases the solubility of nutrients like phosphates and nitrates. This makes these
nutrients more readily available to aquatic plants and algae, which can promote harmful
overgrowth called “algal blooms.” As these blooms die, bacteria numbers increase in
response to the greater food supply. They, in turn, consume more dissolved oxygen from
the water, often stressing or killing fish and aquatic macroinvertebrates (Project Wet
2011).
Also, nitrate is an essential nutrient for aquatic plants and seasonal fluctuations
can be caused by plant growth and decay. Natural concentrations, which seldom exceed
0.1 mg l-1 NO3-N, may be enhanced by municipal and industrial waste-waters, including

leachates from waste disposal sites and sanitary landfills. In rural and suburban areas, the
use of inorganic nitrate fertilizers can be a significant source (Chapman 1996).
To have an overview of the relationship between macroinvertebrates and physico-
chemical parameters, the water quality assessment study using macroinvertebrates and
physico-chemical parameters in the riverine system of Iligan City, Philippines was
studied. As Tampus and other authors of the paper have found out, the results showed
that Diptera, namely Chironomidae, have high presence in the river sampling sites during
dry season. The species is an indicator of potential pollution and can be possibly
attributed by washing and bathing effluent discharges along the river. Trichoptera yet
displays more population, and this species tolerate wide range of environmental
conditions. Also, low macroinvertebrate counts were found to be correlated with the high
levels of phosphate and nitrogen ions. An analysis to determine what physico-chemical
parameters would affect macroinvertebrates assemblage was done and results showed
that Total Suspended Solids (TSS) affect the groups Plecoptera, Tricoptera, Diptera and
Simuliidae while nitrate affects Plecoptera and Gomphidae. Tricoptera and Plecoptera
were known to be sensitive to the conditions of the waters such that any changes in the
concentrations of the chemical components of the water would affect its assemblage. This
study was also related to the water quality monitoring research using benthic
macroinvertebrates and physicochemical parameters of Behzat Stream in Turkey. Results
revealed that there is low summer abundance of few macroinvertebrate fauna in the
Behzat stream due to high values of Phosphate, Ammonia nitrogen, Nitrate and Nitrite
(Duran 2006).

Lastly, to determine the macroinvertebrate composition, diversity and richness in
relation to the water quality status, a research study was done in Mananga River, Cebu,
Philippines. Some of the findings reveal that some of the taxa identified show positive
correlation with pH with respect to its abundance. Also, the overall macroinvertebrate
species richness is positively correlated with pH. The direct correlation of pH with
richness and diversity implies that many species favored an increasingly basic habitat.
Subsequent findings also reveal that there was an increase in total suspended solids (TSS),
water temperature, stream width, water depth, and biological oxygen demand (BOD5),
but decreased flow velocity, pH, and dissolved oxygen (DO) levels in the downstream
area of the said river.
The water quality parameters of Mananga River in the three sampling stations
were significantly different, except for alkalinity, total phosphates, and NO3-N. The
significant changes in these factors could be attributed to natural change in slope gradient,
channel width, water depth and stream bed profile resulting in diminishing flow velocity
and DO, but significantly increasing discharge thereby increasing TSS, and BOD5
downstream. Substrate, suspended sediment, gradient, water temperature, stream order
and width were observed to have significant influence over biomass and diversity of
macroinvertebrates. The USEPA (1976, 1986) indicates that a pH range of 6.5 to 9.0
provides adequate protection for the life of freshwater fish and bottom-dwelling
macroinvertebrates. Some studies report that taxa richness, density of invertebrates and
diversity increased along a river continuum with increases in pH, hardness and nutrients
(Flores and Zafaralla 2012).

Puga-an River Water Quality Assessments
Meanwhile, Puga-an River, the subject of this study, is one of the major
rivers in Iligan City. The landscapes surrounding it vary from forests to agricultural to
urban land uses. Aside from being habitat to many organisms, it is also utilized for
domestic purposes, such as washing and bathing, by the residents nearby. The water
discharges to a major river – Iligan River – before draining into Iligan Bay. Thus, the
river quality will have impact on the organisms living in it and the organisms living in the
Iligan Bay, which is a seafood source of people living in Iligan City (Bedoya 2008).
Some parts of Puga-an River, as studied by previous researchers, were found to
have a rather clean water that time. On 2008, Bedoya conducted an initial assessment on
the river and results showed that the river water quality is rather clean to clean water. A
subsequent research on a certain section of the river was conducted on 2012 by Bedoya
where results also showed that the river quality is rather clean to clean water through the
use of water quality index. However, as time goes by population continue to proliferate
adjacent to the river along with the destructive activities like agricultural production,
quarrying and logging. Considering also the recent natural calamity caused by typhoon
Sendong, the river’s morphology may have also been altered.

CHAPTER III
METHODOLOGY
Locale and Subject of the Study
Barangay of Puga-an is located in Iligan City of the Province of Lanao del Norte,
Mindanao, Philippines. It is bounded on the north by portion of the Barangays Luinab
and Mandulog, on the east by the Municipality of Kapay, on the south by the Barangay
Tipanoy, on the southeast by the Barangay Ubaldo, on the west by Barangay Pala-o and
on the northwest by the Barangay del Carmen (Appendix 1 and Figure 2). The central
part of the barangay is cut across by the Puga-an River. Puga-an River lies within the 8°
13' 43" North latitude and 124° 14' 08" East longitude. The estimated terrain elevation
above sea level is 4 meters (http://ph.geoview.info.html). Iligan City in general has a
significant amount of average rainfall of 3180 mm and its average annual temperature is
24.0 °C. The driest month is April which receives 80 mm of precipitation every year.
With an average of 780 mm, the most precipitation falls in October. With an average of
24.8 °C, May is the warmest month. January has the lowest average temperature of the
year at 22.7 °C (Zednik 2014).

Area 1= Upstream
Area 2= Midstream
Area 3= Downstream
Figure 2. Location map of the study area
Puga-an

Selection of the Study Site
Barangay Puga-an was selected because existing land uses vary from different
sampling sites. Agricultural and urban land-uses greatly alter both the physical and the
chemical aspects of macroinvertebrate habitat, impacting the structure of
macroinvertebrate communities. Land-use change through a chain of indirect effects can
lead to changes to the macroinvertebrate assemblage in both taxa richness and relative
abundance. These relationships between macroinvertebrate communities and stream
ecosystem conditions make community structure a good indicator of overall stream
health (Kenny et.al 2009).
There were three sampling sites chosen (upstream, middlestream and
downstream). Each sampling sites had three replicates each considering riffle and pool
areas. In particular, the sampling sites were located in Purok 4, Purok 2 and Purok 1,
respectively. The upstream area (Purok 4) lies between 51 P 642329, 642293, and
642291 UTM East longitudes and 909965, 90993, and 909885 UTM North latitudes
(Appendix 2). The middle stream (Purok 2) is located between 51 P 640389, 640401,
and 648425 UTM East longitudes and 909781, 909776, and 909774 UTM North latitudes
(Appendix 3). Downstream area (Purok 1) is at 51 P 639744, 639703, and 639667 UTM
East longitudes and 909422, 909410, and 909422 UTM North latitudes (Appendix 4).
It is one of the 19 agricultural barangays of Iligan City. The barangay has 28
puroks and two of those are found in upland (Dalagan and Disomimba 2002, Puga-an
Barangay 2016). Accordingly, the total land area surrounding the river was comprised of
agricultural area (899.21 has.). The residential area is about 80.48 hectares. The
residential/agricultural area is 0.31 hectares. The residential/recreation is 3.61 hectares.

The commercial/residential area is 9.08 hectares. The institutional area is 6.73 hectares.
The institutional/residential is 27.29 hectares. The public forest is 80.48 hectares. The
vacant lot is 1.80 hectares and road is 6.37 hectares (Appendix 5) The Puga-an River
stretches from the highlands of Lanao del Norte along the barangay of Puga-an to
approximately 22 kilometers (Appendix 6) down to Iligan Bay.
Experimental Design and Layout
The replicates of the sampling sites were laid out in a Randomized
Completely Blocked Design (CRBD). The experimental layout (Table 1) and river flow
of the three sampling sites (Figure 3) was presented on the following illustrations.
Table 1. Experimental layout of the study
Upstream --- (distance = 1.70km) --- Midstream --- (distance = 0.894km)--- Downstream
Flow of the River
Figure 3. River flow of the three streams
The sampling sites (Appendix 7) were determined according to human
activities observed around the area and on the depth of the river and adapted from
Bedoya (2008). It is known that the replicated sites identified were riffle and pool areas.
Barangay
Puga-an
Upstream
Purok 4
Sampling Site 1
Midstream
Purok 2
Sampling Site 2
Downstream
Purok 1-D
Sampling Site 3
Sampling
Replicates
R1 R2 R3 R1 R2 R3 R1 R2 R3

Thus, the methods for macroinvertebrates sampling for riffle and pool areas were applied.
The riffle and pool areas were used to represent the macroinvertebrates population of the
river stretch (upper, middle and downstream).
Parameters measured such as stream velocity, depth, temperature, Total
Suspended Solids (TSS), pH, Dissolved Oxygen (DO) level, Biological Oxygen Demand
(BOD) and nitrate (NO3) levels were also determined as supporting data for water quality
assessment of the river. These parameters or characteristics play significant role in the
life cycle of macroinvertebrates.
Experimental Materials
The experimental materials in assessing the water quality of a river are very
important for collecting samples. In conducting this study, the researcher used: (1) plastic
tray, (2) kick net with 500 μm mesh in scooping bottom-dwelling macroinvertebrates, (3)
D-net with a mesh size 500 μm in scooping surface-living macroinvertebrates, (4) drift
net with 500μm mesh in filtering macroinvertebrates in the flowing surface water, (5)
strainer, (6) plastic vials and caps, (7) magnifying glass, (8) digital camera, (9) tweezers,
and (10) measuring tape. In the determination of some of the physical and chemical
parameters of the water the following were used: (11) DO bottles, (12) plastic bottles, (13)
ice chest, (14) thermometer, (15) meterstick, (16) stopwatch, and (17) GPS receiver
(Appendix 8). The “Guide to Freshwater Invertebrates of Ponds and Streams in Thailand”
by Kanjanavanit and Tilling was used to identify in situ the collected macroinvertebrates.

Methods of Data Collection
Macroinvertebrates Survey
The kick net method (Figure 4) was used to sample macroinvertebrates by the
river banks or shallower areas (Jones and Bowker 2014). It was made of two wooden
dowels of about 1.25m long with 2-3cm diameter support a 1x1 –m square of 500μm
mesh netting. This was used by the kicking action made unto the net facing towards the
water current. However, the area sampled was delineated with 1 by 1 frame out of wood.
The delineated area was picked up of cobbles and stones set to sides then vigorously
disturbed via stepping to the framed area and kicking back and forth for about 1 minute
(Hauer and Resh 1996).
Figure 4. Kick net
The dip net or the D frame net (Figure 5) was used to sample
macroinvertebrates in deeper portions of the river by 3 sweep samples distributed out of
the edge of water to the mid-point of the river or until depth exceeds 1 m, each 0.5 m in
length. The net had 500 μm mesh and 0.3 m width (Jones and Bowker 2014). This was
utilized with a 1 by 1 frame out of wood in front of the net and a kicking made back and
forth for 1 minute.

Figure 5. Dip Net
The drift net (Figure 6) was used to collect macroinvertebrates in the flowing
surface water or facing the water current in an hour (Hauer and Resh 1996). It has a 500μ
mesh netting with a dimension of 45cm by 15 cm.
Figure 6. Drift Net
The collected macroinvertebrates were sorted, identified, counted, and scored
right after the sampling day (Figure 7). Those that were not identified on the spot was
brought to the laboratory and identified by an expert.
Figure 7. On-site identification of macroinvertebrates

Physico-Chemical Parameters Water Sampling
Physico-chemical parameters water sampling (Figure 8) was done according
to the laboratory protocols of the MSU-Naawan Research Division: Chemistry
Laboratory and MSU- Main Campus College of Natural Sciences and Mathematics:
Chemistry Department which analyzed the water samples.
Figure 8.Physico-chemical parameters water sampling
For getting the DO and BOD of the river water, DO bottles were used.
Separate plastic bottles were used for getting the nitrates concentration. The analyses
were done according to the procedures employed by the MSU-Naawan Research
Division: Chemistry Laboratory (Appendix 9). The TSS concentration and pH were also
determined by getting water samples and placing them in plastic bottles. Thereafter, the
samples were delivered and analyzed according to the procedures used by MSU- Main
Campus College of Natural Sciences and Mathematics: Chemistry Department
(Appendix 10).

Key Informants Survey
Furthermore, an interview of key informants (Appendix 10) in the area was
done with questions relating to the observations of the river through time (Appendix 12).
Lastly, the study was conducted on March 19 where the average high
temperatures rise to 28.5 °C and fall to 18.6 °C with an average 284 mm of rainfall
(Zednik 2014).
Data Analysis
Water Quality Index
According to the Guide to Freshwater Invertebrates of Ponds and Streams in
Thailand by Kanjanavanit and Tilling (2000), the presence or absence of
macroinvertebrates gave corresponding points regardless of its abundance. The scores of
each macroinvertebrates were summed up and divided by the number of animal types
obtained. The resulting value is the water quality index. With this value, the water quality
of a river assessed based on the following range of scores in the Table 2.
Table 2. Water Quality Index
Scores Water Quality
7.6-10 very clean water
5.1-7.5 rather clean – clean water
2.6-5.0 rather dirty – average
1.0-2.5 dirty water
0 very dirty water (no life at all)

EPT Index
The water quality of the Puga-an River was also analyzed through the EPT
index. This is the number of Ephemeroptera (mayflies), Plecoptera (stoneflies) and
Trichoptera (caddisflies) species found within the river during the sampling. Based on
their population, water quality will be determined because these are sensitive to pollution.
Thus, the higher the EPT species, the better the water quality.
EPT Index Formula:
Ephemeroptera + Plecoptera + Trichoptera
EPT index= _____________________________________________
Total Number of Midges
The midges are the species that can tolerate water pollution.
Shannon-Weiner Diversity Index
For measuring the diversity of the population of the macroinvertebrates
collected, the Shannon-Wiener diversity index (H’) was used with the formula:
H’ = - ∑ pi log pi
Pi is the total number of individuals in the nth species. This index would
calculate the species richness and equitability and is the basis for the determination of
water quality through population of the sensitive species (Hauer and Resh 1996).
Statistical Analysis
The Analysis of Variance (ANOVA) was used for the treatment of variances
of the stream velocity, depth, temperature, TSS, DO, BOD, pH, NO3 raw data (Appendix
13) and Shannon-Weiner diversity indices in the 3 barangays. It was also applied to test

significant differences between physico-chemical characteristics, between physico-
chemical characteristics and macroinvertebrates diversity, between physico-chemical
characteristics and water quality, and between water quality and macroinvertebrates
diversity of the three sampling sites. The treatment means for every analysis was shown
in Appendix 14. To further know the significant differences of the ANOVA results, the
Tukey’s Test was applied. The Chi2
was also used to determine significant difference on
the presence of macroinvertebrates in the three sampling sites of the river. The statistical
softwares used were Paleontological Statistics (PAST) 2.17V for the analysis and
MiniTab 17 for plotting the graphs. Statistical significance accepted within 5% p-value
(significant) was emphasized with one asterisk (*) and within the 1% p-value (highly
significant) was emphasized with two asterisks (**).

CHAPTER IV
RESULTS AND DISCUSSION
Physical Characteristics of the River
Stream Velocity
The velocity, also referred to as the flow rate (distance over time) of any water
body may affect its ability to transport pollutants. Hence, determining of velocity in a
water quality assessment program is necessary (Chapman 1996). In this study, it is
measured in meters per second (m/s).
As shown in Figure 9, the mean velocity recorded in the upstream is 0.12 m/s.
The midstream has a mean velocity of 0.15 m/s. The downstream has a mean velocity of
0.05 m/s.
UpstreamCurMidstreamCurDownstreamCur
0.20
0.15
0.10
0.05
0.00
Data
Interval Plot of DownstreamCu; MidstreamCur; ...
95% CI for the Mean
The pooled standard deviation is used to calculate the intervals.Figure 9. Stream velocity of the three sampling sites
The differences in stream velocities are statistically significant since it has a p-
value of 0.00, which is less than the significant limit value of 0.05 as shown in Table 3.
The downstream velocity statistically varies with the upstream and midstream velocities

(Table 4). The downstream is probably affected by the upstream and midstream sites.
This also shows that the river has an irregular stream flow depending on the site assessed.
Table 3. One-way ANOVA of the stream velocity of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 0.08 2 0.04 8.26** 0.00**
Error 0.22 42 0.01
** highly significant
Table 4. Tukey’s Pairwise Comparisons of stream velocity in the three sampling sites
Upstream Midstream Downstream
Upstream (Purok 4- Guiaon) 0.51 0.02*
Midstream (Purok 2-A) 1.58 0.00**
Downstream (Purok1-D) 3.99 5.58
** highly significant *significant
Rivers usually have relatively high, average flow velocity that ranges from 0.1 to
1 m/s -1
following a single current path. Climatic conditions and drainage patterns makes
river flow vary with respect to time (Chapman 1996). As cited by Chapman (1996), only
on the downstream with major confluences will lateral mixing of water occurs. Thus, the
mean stream velocity may have been interrupted especially the velocity near the
submerged bridge where the downstream sampling site is located. The presence of
various structures surrounding the downstream site could have impacted flow regime.
Furthermore, Yazdian (2014) had mentioned that stream velocity affects
morphology of river beds and movement of sediments which in turn have impacts on
various species including macroinvertebrates.

Depth
In most studies involving macroinvertebrates, water depth or level may play a
role in the life cycle of the many aquatic organisms in the river but is not much focused.
However, for this study, depth (centimeters or cm) was measured to see if there was
correlation between this parameter and the species abundance of macroinvertebrates in
the three sampling sites.
As shown in Figure 10, the highest water depth is obtained at the upstream
with a mean level of 39.67cm. The midstream and downstream sites have mean levels of
21.67cm and 20.5cm, respectively. This means that depth of the river decreases as it
approaches downstream.
UpstreamDEPTHMidstreamDepthDownstreamDepth
60
50
40
30
20
10
0
Data
Interval Plot of DownstreamDe; MidstreamDep; ...
95% CI for the Mean
The pooled standard deviation is used to calculate the intervals.
Figure 10. Water depth of the three sampling sites
The analysis on variation between river depths among the three sampling sites
is shown in Table 5. However, the p-value of the analysis which is 0.2 is not significant.
This could imply that the depth of each site is not affected by the other. Hence, the three
sites do not vary significantly in terms of depth (Table 6).

Table 5. One-way ANOVA of the depth of the three sites
Sum of
Squares
df Mean
Square
F p-level
Effect 692.72 2 346.36 1.70 0.26
Error 1221.83 6 203.64
Table 6. Tukey’s Pairwise Comparisons of depth in the three sites
Upstream (Purok 4- Guiaon) 0.34 0.30
Midstream (Purok 2-A) 2.19 0.99
Temperature
Temperature affects physical, chemical and biological processes in water bodies
and, therefore, the concentration of many variables. Temperature is measured in degrees
Celsius (˚C) for this study.
The mean temperatures for the upstream and midstream sites are both 31.33˚C
and the downstream site has a mean temperature of 29.33˚C (Figure 11).
UpstreamTempMidstreamTempDownstreamTemp
31.5
31.0
30.5
30.0
29.5
29.0
Data
Interval Plot of DownstreamTe; MidstreamTem; ...
95% CI for the Mean
Figure 11. Temperature of the three sampling sites

The p-value of the three sampling sites is highly significant at 0.00 (Table 7).
According to the Tukey’s pairwise comparison on the temperature data of the three
sampling sites (Table 8), the upstream and midstream temperature statistically differ with
the downstream site. This implies that the downstream temperature is probably
influenced by the upstream and midstream temperatures.
Table 7. One-way ANOVA of the temperature of the three sites
Sum of
Squares
df Mean
Square
F p-level
Effect 40 2 20 120** 0.00**
Error 7 42 0.17
Table 8. Tukey’s Pairwise Comparisons of temperature in the three sites
Upstream (Purok 4- Guiaon) 1 0.00**
Midstream (Purok 2-A) 0 0.00**
The time of water sampling is a factor that was considered during data gathering.
The downstream temperature was measured around 8-10 AM, the midstream was
measured at 10-12 noon and the upstream at around 2-4 PM. It can be inferred that the
upstream site only attain a maximum temperature similar to the temperature of the
midstream site at noontime. On a temporal basis, this implies that the upstream area does
not warm faster compared to the downstream and midstream sites. This temperature
discrepancy could be attributed to the factors affecting temperature. Chapman (1996)

noted that the temperature of surface waters is influenced by latitude, altitude, and season,
time of day, air circulation, cloud cover and the flow and depth of the water body.
Total Suspended Solids (TSS)
The Bioworld Support Website (2015) defines the Total suspended solids (TSS)
as a measurement of the turbidity of the water. It can be observed directly since color
changes along with turbidity. Polluted waters are commonly turbid and improvement is
usually marked by greater clarity. However, good and useful waters may be turbid, and
many clean rivers are never clear because they contain fine suspended minerals that never
settle. In this study, the TSS concentration is measured in parts per million (ppm).
In this study, as the river water approaches downstream, TSS concentration
increases (Figure12).
UpstreamTSSMidstreamTSSDownstreamTSS
44
42
40
38
36
34
32
30
Data
Interval Plot of DownstreamTS; MidstreamTSS; ...
95% CI for the Mean
Figure 12. TSS concentration of the three sampling sites
The result of the ANOVA of the TSS in the three sampling sites in Puga-an River
is statistically significant with p-value at 0.02 (Table 9). As shown in Table 10, the
upstream and downstream sites statistically differ. The downstream TSS is probably
influenced by the upstream TSS.

Table 9. One-way ANOVA of the TSS of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 86.22 2 43.11 7.46* 0.02*
Error 34.67 6 5.78
*significant
Table 10. Tukey’s Pairwise Comparisons of TSS in the three sampling sites
*significant
Variation could be attributed to the fact that high TSS in a water body can often
mean higher concentrations of bacteria, nutrients, pesticides, and metals in the water
(Murphy 2007) because suspended particles provide attachment places for these other
pollutants (Michaud 1994).
Chemical Parameters
pH
The pH is a measure of the acid balance of a solution and is defined as the
negative of the logarithm to the base 10 of the hydrogen ion concentration. The pH scale
runs from 0 to 14 (i.e. very acidic to very alkaline), with pH 7 representing a neutral
condition. At a given temperature, pH (or the hydrogen ion activity) indicates the
intensity of the acidic or basic character of a solution and is controlled by the dissolved
chemical compounds and biochemical processes in the solution (Chapman 1996).

Figure 13 shows the varying differences in the pH of the three sampling sites. The
pH increases as the river approaches the upstream. The upstream has a mean pH value of
7.93 while the midstream and downstream have 7.79 and 7.52, respectively.
pH_UpstreampH_Midstreamph_Downstream
8.0
7.9
7.8
7.7
7.6
7.5
Data
Interval Plot of ph_Downstrea; pH_Midstream; ...
95% CI for the Mean
Figure 13. pH of the three sampling sites
The p-value being at 0.00 (Table 11) means significant differences on the
ANOVA result on the analysis of the pH level in the three sampling sites. The sites that
vary are highly significant (Table 12).
Table 11. One-way ANOVA of the pH of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 0.26 2 0.13 307.6* 0.00**
Error 0.00 6 0.00
**significant

Table 12. Tukey’s Pairwise Comparisons of pH in the three sampling sites
Upstream (Purok 4- Guiaon) 0.00** 0.00**
Midstream (Purok 2-A) 12.08 0.00**
Generally, natural waters have a pH of between 5 and 9 and most aquatic
organisms survive in waters within this range. With the exception of some bacteria and
microbes, if pH goes higher or lower than this range, aquatic life is likely to perish. Water
with low pH increases the solubility of nutrients like phosphates and nitrates. This makes
these nutrients more readily available to aquatic plants and algae, which can promote
harmful overgrowth called “algal blooms” (Project WET Foundation 2011).
Dissolved Oxygen (DO)
The concentration of dissolved oxygen is important because it affects the
distribution of freshwater macroinvertebrates. Oxygen is not very soluble in water and its
solubility depends on the temperature (Letort 2010). It is measured in parts per million
(ppm) for this study.
Maximum concentration of DO is recorded in the downstream site of the river at
10.91 ppm, midstream has an intermediate concentration of 10.44 ppm and the lowest
mean DO concentration is 9.09 ppm at the upstream site as shown in Figure 14.

DO_UpstreamDO_MidstreamDO_Downstream
12
11
10
9
8
Data
Interval Plot of DO_Downstrea; DO_Midstream; ...
95% CI for the Mean
Figure 14. DO of the three sampling sites
Statistical variation of the three sampling sites has a p-value of 0.02 (Table 13).
As shown in Table 14, the DO concentration of the upstream and downstream sites
statistically varies. This implies that the downstream DO is possibly affected by upstream
DO.
Table 13. One-way ANOVA of the DO of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 5.33 2 2.68 7.49* 0.02*
Error 2.14 6 0.36
* significant
Table 14. Tukey’s Pairwise Comparisons of DO in the three sampling sites
*significant

The results therefore coincide with the temperature data gathered. The higher the
temperature, the less the oxygen will be dissolved (Kanjanavanit and Tilling 2000).
Highest temperature and lowest DO level was obtained in the upstream site.
According to Chapman (1996), dissolved oxygen (DO) in fresh waters at sea level
ranges from 15 mg l-1
(unit is equivalent to ppm) at 0° C to 8 mg l-1
at 25° C.
Concentrations in unpolluted waters are usually close to, but less than, 10 mg l-1
. Thus, as
results showed, the river is not yet polluted since the midstream and downstream values
are closed to 10 mg l-1
. Only the upstream is below 10 mg l-1
.
Relating nitrate with dissolved oxygen, low oxygen level is not an immediate
result of pollution with waste high in nitrogen. Growth of plants release oxygen into the
water. Nitrate concentration would not be enough to maintain the population growth.
When the plants die off and rot, the decomposing microorganisms use up the oxygen in
the water and reduce the oxygen level (Nuffield Foundation 2011). This probably
explains why the upstream has the lowest DO during the assessment.
Biological Oxygen Demand (BOD)
The biochemical oxygen demand (BOD) is an approximate measure of the
amount of biochemically degradable organic matter present in a water sample. It is
defined by the amount of oxygen required for the aerobic micro-organisms present in the
sample to oxidize the organic matter to a stable inorganic form. The presence of toxic
substances in a sample may affect microbial activity leading to a reduction in the
measured BOD. The conditions in a BOD bottle usually differ from those in a river or
lake (Chapman 1996). It is measured in mg/L or parts per million (ppm) units. It is
simply the DO required for biochemical decomposition of organic compounds and

oxidation of inorganic materials. This is usually conducted on a five-day period (Patil
et.al 2012).
Figure 15 shows the plotted variation of BOD concentration of the three sampling
sites in Puga-an River. There is fluctuation in the trend of the BOD levels of the three
sampling sites. The upstream and downstream have low levels of BOD at 2.36 ppm and
1.68 ppm, respectively. The midstream has the highest BOD concentration at 3.37 ppm.
Figure 15. BOD of the three sampling sites
The p-value of the three sampling sites is 0.28 (Table 15). BOD statistical results
(Table 16) showed no significant differences among the three sampling sites. This
probably means that BOD of a site does not affect the other sites.
Table 15. One-way ANOVA of the BOD of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 4.31 2 2.15 1.59 0.28
Error 8.12 6 1.35

Table 16. Tukey’s Pairwise Comparisons of BOD in the three sampling sites
Upstream (Purok 4- Guiaon) 0.57 0.77
Unpolluted waters typically have BOD values of 2 mg l-1
O3 or less, whereas
those receiving wastewaters may have values up to 10 mg l-1
O2 or more, particularly
near to the point of wastewater discharge (Chapman 1996). Hence, BOD levels of the
river indicate unpolluted waters.
Nitrate (NO3)
The nitrate ion (NO3) is the common form of combined nitrogen found in natural
waters. It may be biochemically reduced to nitrite (NO2) by denitrification processes,
usually under anaerobic conditions. The nitrite ion is rapidly oxidized to nitrate. Natural
sources of nitrate to surface waters include igneous rocks, land drainage and plant and
animal debris. Nitrate is an essential nutrient for aquatic plants and seasonal fluctuations
can be caused by plant growth and decay. Nitrite concentrations in freshwaters are
usually very low, 0.001 mg l-1
NO2-N, and rarely higher than 1 mg l-1
NO2-N (Chapman
1996). In this study the nitrate concentration is measured in parts per million (ppm)
which is equivalent to the milligrams per liter (mg l-1
) unit used in other studies.
The highest level of NO3 was recorded in the upstream at 0.035 ppm, the lowest is
in the downstream at 0.031 and the midstream at the concentration of 0.027 ppm as
shown in Figure 16.

NO3_UpstreamNO3mIDstreamNO3_Downstream
0.040
0.035
0.030
0.025
Data
Interval Plot of NO3_Downstre; NO3mIDstream; ...
95% CI for the Mean
Figure 16. NO3 of the three sampling sites
The ANOVA of nitrate concentration in the three sampling sites is significant
with p-value of 0.03 (Table 17). As shown in the results of Tukey’s test of pairwise
comparisons in Table 18, the NO3 levels statistically differ in the midstream and
downstream sites of the river. This probably means that the downstream nitrate
concentration is affected by the midstream nitrate concentration. However, concentration
in the upstream was attributed to the land-use of the barangay.
Table 17. One-way ANOVA of the NO3 of the three sampling sites
Sum of
Squares
df Mean
Square
F p-level
Effect 8.82-5
2 4.41-5
6.85* 0.03*
Error 3.87-5
6 6.44-6
*significant
Table 18. Tukey’s Pairwise Comparisons of NO3 in the three sampling sites
Upstream (Purok 4- Guiaon) 0.02* 0.26
* significant

Chapman (1996) mentioned that in regions with intensive agriculture, the use of
nitrogen fertilizers and discharge of waste-waters from the intensive indoor rearing of
livestock can be the most significant sources. An estimated 86% of the total land use of
Barangay Puga-an is an agricultural area. According to interviewed key informants and
the barangay basic information as of 2016, plantation in the uplands of coconut, banana,
corn and camote dominates. Also, McKinney and Schoch (2003) mentioned that moving
water dilutes and decomposes pollutants more rapidly than standing water. Thus, it can
be inferred that the upstream area has NO3 level at high concentration since it is closer to
the upland areas, which have plantations that uses organic and chemicals fertilizers.
As Chapman (1996) noted, when influenced by human activities, surface waters
can have nitrate concentrations up to 5 mg l-1
NO3-N, but often less than 1 mg l-1
NO3-N.
Concentrations in excess of 5 mg l-1
NO3-N usually indicate pollution by human or
animal waste, or fertilizer run-off. In cases of extreme pollution, concentrations may
reach 200 mg l-1
NO3-N. Natural concentrations, which seldom exceed 0.1 mg l-1
NO3-N,
may be enhanced by municipal and industrial waste-waters, including leachates from
waste disposal sites and sanitary landfills. In rural and suburban areas, the use of
inorganic nitrate fertilizers can be a significant source. Thus, the upstream site has the
highest nitrate value due to the frequency of agricultural activities surrounding the site.
High nitrate concentration of the site is also correlated to its pH level. As displayed in
Figure 13, the highest pH is in the upstream site. Subsequently, as the pH gets higher or
approaches basicity, solubility of nitrates and phosphates increase.

Test of Significant Difference on the Physico-Chemical Characteristics of Puga-an
River
The different physical and chemical characteristics of the river were compared
through analysis of variance of each characteristic and their relationship with each other
thereby linking connections between characteristics.
As shown in the results of Table 20, the relationship between physico-chemical
characteristics of the river vary significantly with p-value at 0.00. There is significant
difference on the physico-chemical characteristics in the sampling sites of the river.
Hence, the null hypothesis one (Ho1) is rejected.
Table 19. ANOVA of different physico-chemical characteristics of Puga-an River
Sum of
Squares
df Mean
Square
F p-level
Effect 4654.75 7 664.96 40.05** 0.00**
Error 265.62 16 16.60
The physical characteristics depth, temperature and TSS vary statistically with
velocity. This means that velocity probably affects depth, temperature and TSS
parameters of the river. The chemical parameters pH, DO, BOD and NO3 vary
significantly with the physical parameters depth, temperature and TSS. This also means
that depth, temperature, and TSS may have influence on pH, DO, BOD and NO3 (Table
20).

Table 20. Tukey’s Pairwise Comparisons of different physico-chemical characteristics of
Puga-an River
** highly significant * significant
Land uses affect the rivers’ physical status. Quarrying affects drainage and
channelization of the river (Aazami et.al 2015). Thus, velocity significantly varies with
TSS of the river.
Human infrastructure and the road/stream interface is one of the main pathways
for sediment to reach waterways. Stream crossings, often culverts, can alter in-stream
sediment accumulation and the geomorphology of a stream. Hence, the velocity affects
TSS, depth and temperature of the river.
Presence of Macroinvertebrates
Conventional definition of macroinvertebrates states that they are organisms that
retain in a 500μm sieve. Their size makes them identifiable without the need for special
techniques like microscopy, though some organisms can only be identified with the help
of maximized magnification. Among the animals that fall in this group, many are aquatic
indicators and therefore presence indicates levels of pollution (Cranston et.al 1996).
Parameters Velocity Depth Temperature TSS pH DO BOD NO3
Velocity 0.00** 0.00** 0.00** 0.35 0.11 1.0 1
Depth 11.55 0.96 0.14 0.00** 0.00** 0.00** 0.00**
Temperature 12.99 1.44 0.59 0.00** 0.00** 0.00** 0.00**
TSS 15.63 4.09 2.65 0.00** 0.00** 0.00** 0.00**
pH 3.25 8.30 9.74 12.39 1.0 0.75 0.34
DO 4.27 7.28 872 11.37 1.02 0.35 0.11
BOD 1.00 10.55 11.99 14.63 2.25 3.26 0.99
NO3 0.03* 11.58 13.02 15.67 3.281 4.30 1.04

There are ten different orders of macroinvertebrates found in the upstream of the
river with a total of 117 individual species being collected (Table 21). The most abundant
group of invertebrates belong to snails which includes different species like that of the
pagoda snails (Figure 17), freshwater limpets (Figure 18), and gilled snails (Figure 19)
found in the three replicates on the upstream site.
Table 21. Macroinvertebrates Present in Upstream (Purok 4: Guiaon)
Animals Replicate 1 Replicate 2 Replicate 3 Total
Insects : Order Plecoptera 4 4
Insects : Order Trichoptera 1 16 2 19
Insects : Order Megaloptera 1 1
Insects : Order Diptera 15 1 1 17
Insects : Order Coleoptera 2 4 1 7
Insects : Order Odonata 2 4 4 10
Snails : Order Patellogastropoda 1 2 3 6
Snails: Order Caenogastropoda 4 2 5 11
Snails: Order Neotaenioglossa 6 29 6 41
Worms : Order Haplotaxida 1 1
Total 32 62 23 117
Generally, Molluscans where snails belong, are plant-eaters with shells that vary
in shapes. Living in a wide range habitat preference, some species also tolerate pollution.
However, freshwater limpets that looks flattened only survive in fast-flowing, unpolluted
waters. The pagoda snails which have trapdoors that opens to the right are also good
water quality indicators (Kanjanavanit and Tilling 2000).

Figure 17. Pagoda snail from the order Caenogastropoda (pollution-sensitive)
Figure 18. Freshwater limpet from the order Patellogastropoda (pollution-sensitive)
Gilled snails were also present in the site. They have gills to take in oxygen and
live in the water for two-five years. The Macroinvertebrate Factsheet of Virginia
Department of Education (2016) classified them as pollution-sensitive species.

Figure 19. Gilled snail from the order Neotaenioglossa (pollution-sensitive)
Insects from the Order Plecoptera include stoneflies (Figure 20). This Plecopteran
is usually found in fast-flowing waters. They eat plants and other animals and needs lots
of dissolved oxygen supply. They were known to avoid polluted areas, thus their
presence in the upstream site indicate excellent to good water quality of the stream waters
(Kanjanavanit and Tilling 2000). Their size range from 5 to 30 mm (Herbst 2005).
Figure 20. Stonefly from the order Plecoptera (pollution-sensitive)
Meanwhile, caddisflies (Figure 21) which are also pollution sensitive species
were found in the upstream. There are two types of caddisflies, those that are enclosed in

cases and those that are not. Caseless caddisflies usually spin nets to catch their prey and
are called Common Net spinner caddisfly. They are pollution tolerant. Those caddisflies
that are enclosed with stones, silt or plant materials do not like high levels of pollution
(Kanjanavanit and Tilling 2000). These Trichopterans have sizes ranging from 52 to 25
mm (Herbst 2005).
Figure 21. Caddisflies in leaf cases from the order Trichoptera (pollution-sensitive)
Dragonfly larvaes (Figure 22) were found across three replicates of the upstream
site. Physically, they have large jaws and usually have range size of 5 to 40 mm (Herbst
2005). According to Kanjanavanit and Tilling dated 2000, most of these Odonata insects
do not like high levels of pollution. Generally, they feed on insects including mosquitoes
and blackflies, tadpoles and even small fishes. Their life cycle let them live for several
years before turning into adults.

Figure 22. Dragonfly nymph from the order Odonata (pollution-sensitive)
Meanwhile, worms from the subclass Oligochaeta including tubifex worms
(Figure 23) or blood worms were also found in the upstream. Tubifex worms are
segmented living on silt and mud while feeding on dead plants. However, the species
only appeared in 3rd
replicate of the sampling site with only one organism being recorded.
Worms from this order indicates fair-poor water quality as cited by Kanjanavanit and
Tilling 2000.
Figure 23. Tubifex worm from the order Haplotaxida (pollution-tolerant)
Like the Epehemeropterans, Plecopterans and Tricopterans, some insects from
Megaloptera are also pollution sensitive. The dobsonfly (Figure 24) which was found in

the upstream site is a pollution-sensitive species. Larvaes of this species can grow up to
70mm (Kanjanavanit and Tilling 2000).
Figure 24. Dobsonfly from the order Megaloptera (pollution-sensitive)
In the midstream, there were five orders of macroinvertebrates found during the
sampling. Overall total of the organisms counted is 87 (Table 22). The most dominant
group belongs to the insects of the order Odonata where species caught were dragonflies.
Other orders like that of the Coleoptera, Neotaenioglossa and Haplotaxida were
previously found in the upstream. The only order added is worms of the order
Arhynchobdellida (Figure 25).
Table 22. Macroinvertebrates present in midstream (Purok 2: Market Area)
Animals Replicate 1 Replicate 2 Replicate 3 Total
Insects : Order Coleoptera 1 2 2 5
Insects : Order Odonata 7 29 20 56
Snails : Order Neotaenioglossa 9 7 7 23
Worms : Order Arhynchobdellida 1 1 2
Total 18 39 30 87

Figure 25. Leech from the order Arhynchobdellida (pollution-tolerant)
The order Arhynchobdellida includes species like leeches which has flattened
segmented bodies. They are suckers of small insects and snails. Their sizes range from 5
to 40 mm. However, they are tolerant species of pollution and are therefore found in
polluted waters (Herbst 2005). They were present among the two replicates of the
midstream site. Thus, the site is found to be polluted.
The downstream was found to contain seven different macroinvertebrates order.
The total organisms counted were 103 (Table 23).
Table 23. Macroinvertebrates present in downstream (Purok 1-D: Submerged Bridge)
Animals
Replicate
1
Replicate
2
Replicate
3
Total
Insects: Order Diptera 1 1 2
Insects: Order Coleoptera 1 1
Insects: Order Odonata 2 2 7 11
Snails : Order Neotaenioglossa 19 4 55 78
Snails : Order Pulmonata 8 8
Worms : Order Arhynchobdellida 1 1
Total 22 8 74 104

Among the groups that were present, only insects from the orders Coleoptera,
Odonata and Neotaenioglossa were pollution-sensitive. Other orders such as Diptera,
Pulmonata, Haplotaxida and Arhynchobdellida were pollution-tolerant.
Snails under the order Pulmonata include pouch snails (Figure 26). They do not
have plate-like covering over the shell opening. They may also have spiral shells that
open to the left, or shells coiled in one plane or dome or hat-shaped shells without coils
(Schumaker Chadde 2015).
Figure 26. Pouch snails from the order Pulmonata (pollution-tolerant)
Diptera insects are usually pollution-tolerant. The two organisms found in the site
were the horse fly larvae (Figure 27) and crane fly larvae (Figure 28).

Figure 27. Horsefly larvae from the order Diptera (pollution-tolerant)
Crane fly larvae looks like maggots but are segmented. They have gills that are
finger-like at the hind-end and are feeding on detritus of animals and plants. Their size
ranges up to 50mm (Herbst 2005). Generally, Dipterans survive in a very wide range
water condition which includes polluted waters.
Figure 28. Cranefly larvae from the order Diptera (somewhat pollution-tolerant)
Subsequently, the frequency distribution of different macroinvertebrates taxa was
plotted in Figure 29 to compare numbers of occurrence of different taxa across the river.

Plecopter
Trichopte
Megalopte
Diptera
Coleopter
Odonata
Patelloga
Caenogast
Neotaenio
Pulmonata
Haplotaxi
Arhynchob
-8
0
8
16
24
32
40
48
56
64
Frequency
Figure 29. Frequency distribution of different macroinvertebrates groups across
the Puga-an River
Based on the graph that depicts the frequency distribution of the different
macroinvertebrate groups in the Puga-an river, order Neotaenioglossa is found to be the
most abundant macroinvertebrates taxon within the sampling sites. Insects from the order
Odonata followed the rank and Trichoptera is the third. The lowest species population
belongs to the orders Megaloptera and Diptera which were not present at all the three
sampling sites.

Gilled-snails were the organisms belonging to the order Neotaenioglossa. They
belong to the pollution-sensitive macroinvertebrates. These snails were present in the
three sampling sites. This means that the river is still able to support macroinvertebrates
that are pollution sensitive.
Test of Independence on the Presence of Macroinvertebrates on the Three Sampling
Sites
The presence of the macroinvertebrates in the three sampling sites was analyzed
using Chi2
to see if the presence of macroinvertebrates in a site is independent between
sites.
Table 24 shows that upstream and midstream sites vary statistically with p-value
at 0.00. This implies that the number of organisms in the upstream is independent from
the number of organisms in the midstream or vice-versa.
Table 24. Chi2
of the upstream and midstream sites
Site Total
Organisms
df Chi2
p-level
Upstream 117 10 95.10** 0.00**
Midstream 87
Table 25 shows that the Chi2
of the midstream and downstream sites statistically
vary at 0.00 p-value. This implies that the number of organisms in the midstream is
independent from the number of organisms in the downstream or vice versa.

Table 25. Chi2
of the midstream and downstream sites
Site Total
Organisms
df Chi2
p-level
Midstream 87 6 73.24** 0.00**
Downstream 104
Table 26 shows that at p-value of 0.00, the upstream and downstream sites are
statistically significant. This implies that the number of organisms in the upstream is
independent from the number of organisms in the downstream or vice versa.
Table 26. Chi2
of the upstream and downstream sites
Total
Organisms
df Chi2
p-level
Upstream 117 11 78.40** 0.00**
Downstream 104
The three sampling sites were found to have high statistical variation with each
other. Hence, the null hypothesis 2 (HO2) is rejected. The presence of macroinvertebrates
within a sampling site is not affected by the presence of these in another site. However,
some macroinvertebrates species appear in the three sampling sites in different
population count.
Biodiversity Index
The biodiversity of macroinvertebrates in the three sampling sites are determined
through the use of Shannon-Wiener diversity index (H). This concept means that the

PUGA-AN RIVER WATER QUALITY ASSESSMENT USING MACROINVERTEBRATES AND SELECTED PHYSICO-CHEMICAL CHARACTERISTICS AS INDICATORS

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