thesis manuscript_the end

ATENEO DE MANILA UNIVERSITY

DETERMINING THE METALS AND ORGANIC COMPOUND LEVELS IN

RIVERS DRAINING THE SAN MATEO LANDFILL

A THESIS

PRESENTED TO THE FACULTY

OF THE SCHOOL OF SCIENCE AND ENGINEERING

IN PARTIAL FULFILLMENT

OF THE REQUIREMENT FOR THE DEGREE OF

BACHELOR OF SCIENCE IN ENVIRONMENTAL SCIENCE

BY

JEAN KYLE WERNHER C. DELA CRUZ

QUEZON CITY, PHILIPPINES

MAY 2016

2

ABSTRACT

The San Mateo Landfill (SML) was closed on February 16, 2006 to undergo a

rehabilitation program. Discussions regarding the landfill’s reopening were brought

up multiple times afterwards. Meanwhile, a looming water crisis opens the

discussions for the Wawa Dam’s reopening. Therefore, initial assessment of the

Bosoboso River was needed in order to give a vantage point on the feasibility of

reopening either of the two structures.

The study aimed to determine the state of the Bosoboso River after the

landfill’s operation. The study also investigated the lingering effects of leachates

coming from the landfill. The goal was achieved by gathering data on the

physicochemical parameters, the chemical oxygen demand (COD), and the metals (Cu

and Fe) concentrations within the river.

The COD levels ranged from 63 mg/L to 129 mg/L; copper concentrations

ranged from 1.144 ppm to 1.190 ppm; and iron concentrations ranged from 2.002 ppm

to 3.800 ppm. Results revealed elevated levels of COD and metal concentrations

throughout the river. These conclusions were supported by the results from a previous

study (Tangtatco and Wong, 1999) which revealed that there is a huge increase in

level regarding these parameters after the 10-year gap. Such elevated levels made the

water quality of the river very poor and rules out the landfill as the major source of

pollution.

3

ACKNOWLEDGEMENTS

I would like to extend my sincerest gratitude to the following:

To God who provided me with the people that I needed to support me on this

journey. All of my accomplishments would be nothing without Him.

To Dr. Rene Claveria who guided me from choosing a thesis topic to

providing insightful comments on my final manuscript. I will never forget the

patience, understanding, and effort that you showed me throughout this study.

To Dr. Cherry Ringor and Mr. James Araneta who have provided me their

valuable insights to further improve this study. I will also never forget your patience

and understanding you have given me in finishing this study.

To Mr. Doy Andal and Mr. Ariel Torres who provided the correct materials

and equipment I needed and made sure that they are in order.

To the municipality of San Mateo and to the people of Barangay Pintong

Bukawe who gave access on key information regarding the San Mateo Landfill.

To Ms. Weng Aragones who has fully supported me and my blockmates

throughout our college life. I am very thankful for the efforts you have showed in

order for us to graduate on time.

To the Environmental Science faculty who has imparted us the knowledge we

needed not only for the course, but more importantly for the life we would venture out

after college.

To my family who gave me literally everything to finish this study and

graduate on time. Your sacrifices will not be put in vain.

To Renzo Aguas, Bryan Erfe, Sophia Dayrit, Leandro Kintanar, Brian Lim,

Joel Magturo, Nathaniel Ragas, Romuald Santos, Jonathan Santos, and Maricca Suba

who went out of their way just to help me in this study, in other requirements, and in

other issues.

To the rest of Block MM (2016) who showed their utmost support during

these trying times. I would have given up if you were not there to cheer me up. We

finally did it guys.

As this journey draws to its conclusion, I can only say that I would not be where I am

now without you people. Thank you for your contributions.

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TABLE OF CONTENTS

ABSTRACT ............................................................................................................................... 2

ACKNOWLEDGEMENTS ....................................................................................................... 3

LIST OF TABLES ..................................................................................................................... 6

LIST OF FIGURES ................................................................................................................... 7

LIST OF APPENDIX ................................................................................................................ 8

I. INTRODUCTION .................................................................................................................. 9

1.1 Background of the Study ................................................................................. 9

1.2 Statement of the Problem ................................................................................ 9

1.3 Objectives ...................................................................................................... 10

1.3.1 General Objective .................................................................................. 10

1.3.2 Specific Objectives ................................................................................ 10

1.4 Rationale........................................................................................................ 10

1.5 Scope and Limitation .................................................................................... 11

1.6 Conceptual Framework ................................................................................. 11

II. REVIEW OF RELATED LITERATURE .......................................................................... 12

2.1 History of San Mateo Landfill ...................................................................... 12

2.2 Rehabilitation Program ................................................................................. 13

2.3 Water Quality Standards ............................................................................... 14

2.4 Land use around the San Mateo Landfill ...................................................... 15

2.5 Effects of leachates from the San Mateo Landfill ......................................... 16

2.6 Major Parameters .......................................................................................... 18

2.6.1 Metals ..................................................................................................... 18

2.6.2 Chemical Oxygen Demand (COD) ........................................................ 19

2.7 Minor Physicochemical Parameters .............................................................. 20

2.7.1 Water Temperature ................................................................................ 20

2.7.2 pH ........................................................................................................... 20

2.7.3 Conductivity ........................................................................................... 21

2.7.4 Turbidity ................................................................................................ 21

2.7.5 Salinity ................................................................................................... 22

2.7.6 Dissolved oxygen ................................................................................... 23

III. MATERIALS AND METHODS ....................................................................................... 24

3.1 Sampling Area ............................................................................................... 24

3.2 On-site measurements and sample collection ............................................... 25

3.2.1 Physicochemical Parameters .................................................................. 25

3.2.2 Sample Collection and Preservation ...................................................... 26

3.3 Laboratory Analysis ...................................................................................... 26

5

3.3.1 COD Analysis ........................................................................................ 26

3.3.2 Metals Analysis ...................................................................................... 26

3.4 Data Analysis ................................................................................................ 27

IV. RESULTS AND DISCUSSION ........................................................................................ 28

4.1 Minor Physicochemical Parameters .............................................................. 28

4.1.1 Water Temperature ................................................................................ 28

4.1.2 pH ........................................................................................................... 30

4.1.2 Conductivity ........................................................................................... 32

4.1.3 Turbidity ................................................................................................ 34

4.1.4 Salinity ................................................................................................... 36

4.1.5 Dissolved Oxygen .................................................................................. 38

4.2 Major Parameters .......................................................................................... 40

4.2.1 Chemical Oxygen Demand .................................................................... 40

4.2.2 Metals ..................................................................................................... 42

V. CONCLUSION AND RECOMMENDATION .................................................................. 46

5.1 Conclusion ..................................................................................................... 46

5.2 Recommendation ........................................................................................... 46

REFERENCES ........................................................................................................................ 48

6

LIST OF TABLES

Table 1 EMB’s Guidelines for Compliance to NSWMC Resolution No. 5 13

Table 2 DAO 34-90 Freshwater Classification 14

Table 3 Summary of Water Quality Standards for Class C Waters 15

Table 4 Mean COD Levels Across Sites and Time 17

Table 5 Mean Cu Concentrations Across Sites and Time 17

Table 6 Mean Fe Concentrations Across Sites and Time 17

Table 7 Summary of Sample Collection and Preservation 26

7

LIST OF FIGURES

Figure 1 Conceptual Framework of the Study 11

Figure 2 Land Use Map of San Mateo 16

Figure 3 Sampling Area 24

Figure 4 Mean Water Temperature Values Across Sites (March 6) 28

Figure 5 Mean Water Temperature Values Across Sites (March 19) 29

Figure 6 Mean pH Levels Across Sites (March 6) 30

Figure 7 Mean pH Levels Across Sites (March 19) 31

Figure 8 Mean Conductivity Levels Across Sites (March 6) 32

Figure 9 Mean Conductivity Levels Across Sites (March 19) 33

Figure 10 Mean Turbidity Levels Across Sites (March 6) 34

Figure 11 Mean Turbidity Levels Across Sites (March 19) 35

Figure 12 Mean Salinity Levels Across Sites (March 6) 36

Figure 13 Mean Salinity Levels Across Sites (March 19) 37

Figure 14 Mean DO Levels Across Sites (March 6) 38

Figure 15 Mean DO Levels Across Sites (March 19) 39

Figure 16 Sampling Sites with Corresponding Mean COD Readings 41

Figure 17 Mean COD Levels Across Sites 41

Figure 18 Sampling Sites with Corresponding Mean Cu Concentrations 43

Figure 19 Mean Cu Concentration Values Across Sites 43

Figure 20 Sampling Sites with Corresponding Mean Fe Concentrations 45

Figure 21 Mean Fe Concentration Values Across Sites 45

8

LIST OF APPENDIX

Appendix I Physicochemical Data and Detailed Results 50

Appendix II COD Raw Data and Detailed Results 63

Appendix III Cu Calibration Curve, Raw Data, and Detailed Results 65

Appendix IV Fe Calibration Curve, Raw Data, and Detailed Results 68

Appendix V Laboratory Procedures 71

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I. INTRODUCTION

1.1 Background of the Study

Since its termination on February 16, 2006, the San Mateo Landfill (SML), is

a municipal solid waste sanitary landfill located in Barangay Pintong Bukawe, San

Mateo has been closed for approximately 10 years. During its span of operation, the

106 - hectare landfill received approximately 5,500 to 6,000 cubic meters of garbage

daily from Metro Manila and four Rizal towns (Tangtatco and Wong, 1999). It was

reported that the leachate from the landfill is treated by a series of seven settling

ponds before it reaches the Bosoboso River through an unnamed creek (Figure 2)

(Tangtatco and Wong, 1999).

The Bosoboso River is connected to the Wawa River which is the primary

source of potable water of Metro Manila during 1909 to 1968 (Tangtatco and Wong,

1999). Ubac (1977) stated that the operation of Wawa Dam was terminated by the

Philippine Inter-Agency Committee in 1968 due to the structures instability.

The reopening of SML has been proposed by the Metropolitan Development

Authority (MMDA) in 2005 (Canlas, 2005; Villanueva, 2006). With the rising

demand for a source of drinking water, plans of reopening the Wawa Dam is

discussed (Villanueva, 2006). Due to these reasons, a need to assess the quality of the

Bosoboso-Wawa River system is needed.

1.2 Statement of the Problem

There was a need for an assessment of the current state of the Bosoboso River

and of the effluents draining from SML after the termination of its activities on 2006.

Assessing the effects through time of the effluents draining the landfill on the

ecosystem of the Bosoboso River was also needed in order to identify intervening

measures for the river.

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1.3 Objectives

1.3.1 General Objective

The main objective of this study was to determine if the closed San Mateo

Landfill is a source of pollution and measure the water quality of the river, thus

evaluating the lingering effects of leachates on the Bosoboso River.

1.3.2 Specific Objectives

In order to accomplish the main objective, the study aimed to:

1. Obtain and analyze samples of the leachate stream of the closed San Mateo

landfill and assess their effects on the current status of the Bosoboso River. These

objective can be done by measuring the physicochemical parameters such as pH,

turbidity, conductivity, salinity, dissolved oxygen, chemical oxygen demand

(COD), and metal concentrations such as iron (Fe) and copper (Cu) within the

sites.

2. Determine if there are changes in time regarding the chemical oxygen demand and

as well as the Fe, and Cu, concentrations of the Bosoboso River; thus assessing

any change that occurred after 10 years.

3. Obtain data regarding the effluent discharges from the SML during its operation

from 1989 to 1999 and its effect on the Bosoboso River.

1.4 Rationale

Studies regarding the effects of rehabilitation followed by the closure of SML

to the state of the Bosoboso River and other affected river systems have not been

considered before. Studying the current state of the river will give insight on the

current status of the river. Results from this study may also provide a crucial point

during the discussions of reopening the landfill and utilizing Bosoboso River as a

drinking water source.

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1.5 Scope and Limitation

The major parameters that were measured were COD, Fe, and Cu. The

supporting physicochemical parameters were also limited to DO, water temperature,

pH, conductivity, turbidity, and salinity.

Sampling sites were confined to Figure 2. Sampling dates were conducted on

March 6 and March 19. Sites A and B were covered in March 6 and Sites C, D, and E

were covered in March 19. This setup was done because of time and budget

constraints.

1.6 Conceptual Framework

Figure 1 shows the conceptual framework that the study followed. In order to

assess the effects of the leachates on the quality of the river, the parameters to be

analyzed were set into two categories. The major parameters (COD, Fe, and Cu)

covered the levels of metals and organic matter present in the leachate stream and in

the river while the minor parameters (DO, water temperature, pH, conductivity,

turbidity, and salinity) covered the physicochemical properties that determined the

water quality of the river.

Figure 1. Conceptual Framework of the Study

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II. REVIEW OF RELATED LITERATURE

2.1 History of San Mateo Landfill

The San Mateo Landfill (SML) started operating on February 19, 1990 after

an agreement between the Department of Environment and Natural Resources

(DENR), Department of Public Works and Highways (DPWH), and the governor of

Metropolitan Manila Commission was made in 1988 (Canlas, 2005). In retaliation to

the said agreement, the Sangguniang Bayan ng San Mateo approved a resolution to

ban the creation of the landfill; however, the landfill was still constructed.

SML was used to address the garbage crisis in 1995 (Canlas, 2005). The

landfill continued operating even after the residents of Pintong Bukawe have appealed

for its closure. The fact that the location of the landfill is within the borders of the

Marikina Watershed Reservation, now called Upper Marikina River Basin Protected

Landscape did not matter.

In lieu to the issue regarding the location of the landfill and the need to

mitigate the ongoing garbage crisis, former president Fidel V. Ramos, through the

office of the president, declared Proclamation No. 635 on August 28, 1995 (Canlas,

2005). This proclamation excluded 1,060,529 square meters of the land from the

watershed which was used to continue the operations of the landfill (Canlas, 2005).

During the operations of SML, it was reported that leachates originating from

the landfill were flushed into the Bosoboso River via an unnamed creek, causing the

river to be polluted with metals and organic matter (Tangtatco and Wong, 1999).

The landfill continued operating for another 5 years until the Ecological Solid

Wastes Act of 2000 (R.A. 9003) January 26, 2001. This law mandated all open

dumpsites to be closed (Villanueva, 2015). On February 16, 2006, SML has officially

terminated its operations and started its rehabilitation program (Villanueva, 2015).

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2.2 Rehabilitation Program

Under R.A. 9003, all landfills were forced to close and were subjected to

rehabilitation due to the detrimental effects they caused. Open landfills should have

been closed or converted to controlled landfills on February 16, 2004 and all

controlled dumpsites should have been closed on February 16, 2006. Landfills were

required to comply with the requirements of the National Solid Waste Management

Commission (NSWMC) Resolution No. 5 or the “Adoption of the Guidelines on the

Closure and Rehabilitation of Disposal Facilities” for preparation in transitioning to a

Categorized Sanitary Landfill. As such, the San Mateo Landfill was required to follow

the following guidelines.

Table 1. EMB’s Guidelines for Compliance to NSWMC Resolution No. 5 (Tolentino, 2006)

1

This authority is valid only for the closure and rehabilitation of the identified

controlled dump facility project having a cumulative area of thirty thousand

(30,000) square meters wherein ten thousand (10,000) square meter portion

thereof form part of identified waste enhancement area for maintenance,

rehabilitation, filling ... being part of compliance phase and preparatory transition

to a categorized sanitary land filling;

2

Protective wall/perimeter fence with litter fence, net and vegetated buffer zone

must be constructed/installed and maintained along the site periphery to ensure

that wastes are contained within the area designated for disposal, adequate

vegetative cover should also be provided on the completed phases/sections of the

controlled dump facility;

3 Signages shall be posted at conspicuous portions of the facility indicating work

in progress;

4

Indiscriminately dumped waste including waste scattered at the adjacent

intermittent creek shall be moved and confined to a small area within the active

dumping phase, and at the same time waste may be used as filling material for

depressed areas and/or for required slope attainment provided, however that soil

covering of at least 6 inches thick shall be applied on the exposed waste at the

end of each working day;

5

Wastes picking activity should be confined at the MRF at scheduled working

hours in order not to hamper the operation of the facility as well as to prevent

accidents and that only waste pickers authorized by the grantee shall be allowed

in the facility to maintain control on site operation;

6 Slope gradient should be adjusted, maintained and stabilized in order to prevent

any possible slope failure (1:3 or either 22o to 33

o);

7 Buffer zone of at least three (3) meters from the adjacent intermittent creek shall

be established and maintained;

8 Final soil covering of at least 0.6 meters in thickness at closure and post closure

maintenance, drainage and vegetation shall be maintained throughout the project

14

longevity;

9

Diversion canals or ditches should be installed along the periphery of the

completed cells to prevent surface run-off away from the active disposal area , as

well as to prevent ponding of rain water;

10

Monitoring wells located upstream and downstream shall be provided throughout

the project longevity to serve as water sampling stations for ground water quality

monitoring;

11 Open burning wastes are strictly prohibited;

12

A program of work with the corresponding timelines shall be submitted to this

Office wherein the same shall serve basis for the conduct of monitoring by the

PENRO/CENRO and/or this Office;

13

Any authorized personnel of the DENR/EMB shall have the right to enter and

have access to the records required for reference pursuant to the provisions of

this Authority and RA 9003;

14 This authority should be posted in a conspicuous location clearly visible to the

public and shall be adequately framed and protected against damage;

15

The submitted rehabilitation plan shall be strictly implemented and that any

changes developed therein with respect to the result of regular and progress

monitoring conducted shall be observed; and

16

Other permits/clearances which may be deemed significant and substantial in the

foregoing operation should be properly secured from the concerned government

offices/agencies.

2.3 Water Quality Standards

The Department of Environment and Natural Resources (DENR), through

Administrative Order No. 34, Series of 1990 or the “Revised Water Usage and

Classification/Water Quality Criteria Amending Section Nos. 68 and 69, Chapter III

of the 1978 NPCC Rules and Regulations” (DAO 34-90), classifies water bodies

according to their use and implemented water quality standards for each category to

maintain a safe and satisfactory condition according to their best usage. Table 1 shows

the classifications of inland waters.

Table 2. DAO 34-90 Freshwater Classification (DENR, 1990)

Class Beneficial Use

Class AA

Public Water Supply Class I. This class is intended

primarily for waters having watersheds which are

uninhabited and otherwise protected and which

require only approved disinfection in order to meet

the National Standards for Drinking Water (NSDW)

of the Philippines.

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Class A Public Water Supply Class II. For sources of water supply that will require complete treatment in order to

meet the NSDW.

Class B

Recreational Water Class I. For primary contact

recreation such as bathing, swimming, skin diving,

etc.

Class C

1) Fishery Water for the propagation and growth of

fish and other aquatic resources;

2) Recreational Water Class II

3) Industrial Water Supply Class I

Class D

1) For agriculture, irrigation, livestock watering, etc.

2) Industrial Water Supply Class

3) Other inland waters, by their quality, belong to this

classification.

DENR also implemented Administrative Order 35 or the “Revised Effluent of

1990, Revising and Amending the Effluent Regulations of 1982” (DAO 35-90) to

abide to the provisions of Section 6 (i) of Presidential Decree No. 984 also known as

the “Pollution Control Decree of 1976” and to align with the virtue of Executive

Order No. 192, Series of 1987. DAO 35-90 sets standards on the effluent discharges

of different industries and establishments, such as the San Mateo Landfill, to water

bodies. Table 2 shows the water quality standards imposed by DAO 34-90 and DAO

35-90.

Table 3. Summary of Water Quality Standards for Class C Waters

Standards

Parameters DAO 34-90 DAO 35-90

Cu 0.05 ppm -

COD - 60 mg/L

pH 6.5 - 8.5 6.0 - 9.0

DO 5.0 mg/L -

2.4 Land use around the San Mateo Landfill

In Figure 1, the San Mateo Landfill located in Barangay Pintong Bukawe falls

under the light industrial-mixed use. The land is used both as a residential area for the

residents of the barangay and as an industrial area for the landfill activities. Due to

16

R.A. 9003, the land within the vicinity of the landfill will not be used for other

purposes and will be subjected to rehabilitation.

Figure 2. Land use map of San Mateo (San Mateo Comprehensive Land Use Plan, 2010)

2.5 Effects of leachates from the San Mateo Landfill

During the operations of the San Mateo Landfill on 1988 to 1999, Tangtatco

and Wong (1999) conducted a study regarding the effects of leachates discharged by

the landfill to the receiving waters. From August to December 1998, this study

monitored the SML's discharge of metals and organic matter into the Bosoboso River

by determining their concentration and calculating the pollution load. This study also

monitored the effects of such chemicals on the water quality of the river by measuring

its physicochemical parameters. The study considered the possibility of other

pollution sources other aside from the reported treated leachate stream. Site A is the

dumping site of the SML; Site B is the leachate stream; Site C is the upstream of the

river; and Site D is the downstream of the river. Tables 4, 5, and 6 show the mean

COD, Cu, and Fe values for each month respectively.

17

Table 4. Mean COD Levels Across Sites and Time (Tangtatco and Wong, 1999)

COD Level (mg/L)

Sample 29-Aug-98 26-Sep-98 31-Oct-98 29-Nov-98 21-Dec-98 Average

Site A - 1943 1884 1350 2555 1933

Site B 3910 2560 2178 1073 1708 2286

Site C 17.44 14.98 2.833 24.18 12.87 14.46

Site D 110.1 106.2 71.67 39.42 118.2 89.12

Table 5. Mean Cu Concentrations Across Sites and Time (Tangtatco and Wong, 1999)

Cu Concentration (ppm)

Samples 29-Aug-98 26-Sep-98 31-Oct-98 29-Nov-98 21-Dec-98 Average

Site A - 0.01 0.017 0.0099 0.039 0.018975

Site B 0.036 0.184 0.019 0.015 0.016 0.054

Site C 0.013 0.0059 0.0052 0.002 0.0079 0.0068

Site D 0.011 0.252 0.0052 0.002 0.013 0.05664

Table 6. Mean Fe Concentrations Across Sites and Time (Tangtatco and Wong, 1999)

Fe Concentration (ppm)

Samples 29-Aug-98 26-Sep-98 31-Oct-98 29-Nov-98 21-Dec-98 Average

Site A - 4 2.25 2.09 3.18 2.88

Site B 7.63 9.41 8.64 8.68 7.34 8.34

Site C 1.68 0.9 1.22 0.067 2.88 1.35

Site D 1.72 1.92 1.2 0.312 1.85 1.40

This study found out that (1) significant monthly variations were observed

regarding the concentration of metals in the water system, (2) significant geographical

variations in concentration of all major parameters were observed in which the

recipients of the leachates are reported to have a higher concentration levels, (3) there

are other sources of pollution besides the leachate ponds in which they can be

assumed as leaks from the landfill, and (4) the organic levels are significantly higher

downstream than upstream and that the pollutants coming from the landfill and the

unknown pollutant source significantly deteriorates the water quality of the Bosoboso

River (Tangtatco and Wong, 1999).

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2.6 Major Parameters

2.6.1 Metals

Metals in river systems come from natural and artificial sources. Metals that

are naturally introduced into the rivers come from rock weathering, soil erosion, or

the dissolution of water-soluble salts (Garbino et al., 1995). On the other hand, typical

anthropogenic sources of heavy metals are municipal wastewater treatment plants,

manufacturing industries, mining and rural agricultural cultivation and fertilization

(Garbino et al., 1995). Heavy metals may be volatilized to the atmosphere or stored in

riverbed sediments while toxic heavy metals (especially those dissolved in water) can

be taken up by organisms, causing the most deleterious effects (Garbino et al., 1995).

Metals can be either transported with the water and suspended sediment or

stored within the riverbed bottom sediments. Heavy metals are transported as (1)

dissolved species in the water, (2) suspended insoluble chemical solids, or (3)

components of the suspended natural sediments (Garbino et al., 1995). Metals

dissolved in the water can exist as hydrated metal ions or as aqueous metal complexes

with other organic or inorganic constituents (Garbino et al., 1995). Water-insoluble

inorganic (non-carbon-containing, except for carbonates) chemical solids such as

metal hydroxides may be formed, as may organic (carbon-containing) chemical

solids, such as those associated with compounds derived from the decay of living

organisms (Garbino et al., 1995). Both inorganic and organic solids can be transported

with the water as individual entities or as chemical coatings on suspended sediments

(Garbino et al., 1995). In addition, mineral components of suspended sediments

themselves can contain heavy metals (Garbino et al., 1995). Heavy metal solids can

also be stored in river-bottom sediments. Suspended sediments and metallic chemical

solids are stored in riverbed sediment after they aggregate to form large, denser-than-

19

water particles that settle from the water when the river's flow is not sufficient to keep

them in suspension (Garbino et al., 1995).

2.6.1.1 Iron

By mass, iron is the fourth most abundant element in the Earth’s crust

(“Iron,”2015). This metal is used to manufacture steel, used in civil engineering

(reinforced concretes, girders, and etc.) and in manufacturing (“Iron,”2015). Iron is an

essential element for all forms of life and is non-toxic. The average human contains

about 4 grams of iron and most of it is found in the hemoglobin (“Iron,”2015).

Humans need about 10 - 18 milligrams of iron each day and a lack of iron in the body

will cause anemia to develop (“Iron,”2015).

2.6.1.2 Copper

Most copper is used in electrical equipment because of its ductility and

conductivity to heat and electricity (“Copper,”2015). It is also used in construction

(roofing and plumbing), and in industrial machinery (heat exchangers)

(“Copper,”2015). Copper is an essential element in plant and animal life. To help

enzymes transfer energy in cells, an adult human needs around 1.2 milligrams of

copper a day (“Copper,”2015). However, excessive copper is toxic to the body. DAO

34-90 has set 0.05 ppm as the acceptable concentration for Class C waters.

2.6.2 Chemical Oxygen Demand (COD)

COD is a measure of the capacity of water to consume oxygen during

decomposition of organic matter and the oxidation of inorganic chemicals such as

ammonia and nitrite ("Chemical Oxygen Demand,"2015). DAO 35-90 implements a

maximum COD level of 150 mg/L for effluents discharged from old structures such

as SML in Class C waters.

20

2.7 Minor Physicochemical Parameters

2.7.1 Water Temperature

Water temperature is a physical property expressing how hot or cold water is.

Since hot and cold are both subjective terms, temperature can further be defined as a

measurement of the average thermal energy of the water (Kemker 2014). Thermal

energy is the kinetic energy of atoms and molecules, so temperature in turn measures

the average kinetic energy of the atoms and molecules (Kemker 2014).

Water temperature is an important factor which affects the metabolic rates and

biological activity of aquatic organisms as well as influence several other parameters

such as photosynthesis production, compound toxicity, dissolved oxygen and other

dissolved gas concentrations, conductivity, salinity, oxidation reduction potential

(ORP), pH, and water density (Kemker 2014).

2.7.2 pH

pH is a measure of how acidic or basic the water is. It quantifies the relative

amount of free hydrogen and hydroxyl ions in the water (“pH,” n.d.). Water that has

more free hydrogen ions is acidic while water that has more free hydroxyl ions is

basic (“pH,” n.d.).

pH is an important indicator of chemical change because it can be affected by

chemicals in water. The pH of water determines the solubility and biological

availability of chemical constituents such as nutrients (phosphorus, nitrogen, and

carbon) and heavy metals (lead, copper, cadmium, etc.) (“pH,” n.d.).

DAO 34-90 implements a pH range of 6.5 to 8.5 for Class C waters. On the

other hand, DAO 35-90 implements a pH range of 6.0 - 9.0 for effluents coming from

SML. Majority of aquatic organisms prefer a pH range of 6.5 – 9.0. As pH levels

21

move away from this range, the more stress the organism will experience – reducing

the hatching and survival rates while increasing the mortality rate (Kemker 2013).

The more sensitive a species, the more affected it is by changes in pH.

2.7.3 Conductivity

Conductivity is a measure of water’s capability to pass electrical flow which is

directly related to the concentration of ions in the water. These conductive ions come

from dissolved salts and inorganic materials such as alkalis, chlorides, sulfides and

carbonate compounds (Kemker 2014).

In addition to being the basis of most salinity and total dissolved solids

calculations, conductivity is an early indicator of change in a water system. Most

bodies of water maintain a fairly constant conductivity that can be used as a baseline

of comparison to future measurements (Kemker 2014). A sudden increase or decrease

in conductivity in a body of water can indicate pollution (Kemker 2014). Agricultural

runoff or a sewage leak will increase conductivity due to the additional chloride,

phosphate and nitrate ions while an oil spill or addition of other organic compounds

would decrease conductivity as these elements do not break down into ions (Kemker

2014). In both cases, the additional dissolved solids will have a negative impact on

water quality (Kemker 2014).

2.7.4 Turbidity

Turbidity is a criterion that quantifies the opaqueness of a water sample.

Opaqueness of a water sample is due to suspended colloids with a diameter of at least

0.1 micrometers which is mostly consist of clay, silt, organic matter, planktons, and

other microorganisms (Eaton et al., 1995).

Turbidity measurements are often used as an indicator of water quality based

on clarity and estimated total suspended solids in water. The turbidity of water is

22

based on the amount of light scattered by particles in the water column (Kemker

2014). The more particles that are present, the more light that will be scattered. As

such, turbidity and total suspended solids are related. However, the negative

interference can also be caused by either high colored suspended particles like

activated carbon or dissolved colored substances like dyes (Eaton et al., 1995).

Turbidity is the most visible indicators of water quality. These suspended

particles can come from soil erosion, runoff, discharges, stirred bottom sediments or

algal blooms (Kemker 2014). Clear water is usually considered an indicator of healthy

water even though it is possible for some streams to have naturally high levels of

suspended solids. A sudden increase in turbidity in a previously clear body of water is

alarming such as excessive suspended sediment can impair water quality for aquatic

and human life, impede navigation and increase flooding risks (Kemker 2014).

2.7.5 Salinity

Salinity is the total concentration of all dissolved salts in water (Kemker

2014). These electrolytes form ionic particles as they dissolve, each with a positive

and negative charge (Kemker 2014). As such, salinity is a strong contributor to

conductivity. More often, salinity is not measured directly, but is instead derived from

the conductivity measurement and is commonly known as practical salinity (Kemker

2014).

Salinity is important in particular as it affects dissolved oxygen solubility. The

higher the salinity level, the lower the dissolved oxygen concentration. Oxygen is

about 20% less soluble in seawater than in freshwater at the same temperature

(Kemker 2014). The effect of salinity on the solubility of dissolved gases is due to

23

Henry’s Law; the constant used will changes based on salt ion concentrations

(Kemker 2014).

2.7.6 Dissolved oxygen

Dissolved oxygen refers to the level of free, non-compound oxygen present in

water or other liquids (Kemker 2013). It is an important parameter in assessing water

quality because of its influence on the organisms living within a body of water. A

dissolved oxygen level that is too high or too low can harm aquatic life and affect

water quality. DAO 34-90 implements a minimum DO level of 5.0 mg/L for Class C

waters.

The actual amount of dissolved oxygen (in mg/L) will vary depending on

temperature, pressure and salinity (Kemker 2013). First, the solubility of oxygen

decreases as temperature increases. This means that warmer surface water requires

less dissolved oxygen to reach 100% air saturation than deeper cooler water (Kemker

2013). Second dissolved oxygen decreases exponentially as salt levels increase. That

is the reason why, at the same pressure and temperature, saltwater holds about 20%

less dissolved oxygen than freshwater (Kemker 2013).

24

III. MATERIALS AND METHODS

3.1 Sampling Area

Five sampling sites were selected for this study – one site at the leachate

stream and four sites at the Bosoboso River (Figure 2). The purpose of choosing these

sites is to determine the water quality parameters of the leachate stream and of the

Bosoboso River. Sites C, D, and E were chosen to replicate the sampling sites used by

Tangtatco and Wong (1999) in order to look at the change happened over time.

Figure 3. Sampling Area

Site A: Downstream of Bosoboso River

Site A is characterized by a slightly deep, yellow-greenish stream with slightly

strong pungent odor and a rocky terrain with the presence of gabi along the

banks. The site is home to janitor fishes and is isolated from anthropogenic

activities aside from the usual campers.

Site B: Upstream of Bosoboso River

Site B is characterized by a slightly greenish shallow stream with a mild

pungent scent. Presence of vegetation (gabi and kangkong) as well as litter

25

along the banks can also be observed. This site is near a community and is

along a paved road.

Site C: Leachate stream connecting to Bosoboso River

Site C is characterized by a clear and very shallow waterway under the shade

of trees. Litters ranging from dried leaves to diapers and plastic bags were

found along the banks of the river.

Site D: Downstream after the confluence

Site D is found after the confluence of the leachate stream and the Bosoboso

River. This site is characterized by a green fast flowing slightly deep stream

and a rocky terrain filled with gabi along the banks.

Site E: Upstream before the confluence

Site E is found before the confluence of the leachate stream and the Bosoboso

River. This site is characterized by a green fast flowing deep stream with a

muddy terrain filled with gabi along the banks.

3.2 On-site measurements and sample collection

3.2.1 Physicochemical Parameters

Measuring the water quality parameters such as pH, conductivity, turbidity,

and salinity was done in situ using HORIBA U-10 meter and probe water quality

checker. Other water quality parameters such as dissolved oxygen and temperature

were measured using the DO meter. On-site observations were also noted.

26

3.2.2 Sample Collection and Preservation

Sampling collection for each site was done once – March 6 for Site A and B

and March 19 for Site C, D, and E. The procedures that were used in the collection

and preservation of samples are summarized in Table 7.

Table 7. Summary of Sample Collection and Preservation

Condition Metals COD

Collection Type Mid depth water sampling

Sample Containers Polyethylene bottles and amber bottles

Sample Quantity per

Replicate 1 L 500 mL

Number of Sample

Replicates 3 3

Sample Preservation Acidify with HNO3 until

pH < 2

Acidify with H2SO4 until

pH < 2

3.3 Laboratory Analysis

3.3.1 COD Analysis

Eaton et al. (1995) suggested using the open reflux method for analyzing

COD; however, the reactor digestion method (Method 8000) of the HACH DR/890

Portable Colorimeter was used due to their similarity in sample handling (Appendix).

This method was used to measure the levels of organic activity present in the leachate

stream and in the river.

3.3.2 Metals Analysis

Eaton et al. (1995) suggested using the Direct Air-Acetylene Flame Atomic

Absorption Spectroscopy (FAAS) Method in the Standard Methods to determine the

concentration of the concerned metals (Appendix). Method 3005 in the Standard

Methods was used in the digestion of water for recoverable or dissolved metals in

preparation for the FAAS analysis (Appendix).

27

3.4 Data Analysis

Bar graphs and statistical tools were used to analyze both the field and

laboratory results. Bar graphs gave visual information on the general trends on the

parameters measured. The graphs also illustrated whether the current levels of the

parameters fall under the considered standards.

Results from Tangtatco and Wong (1999) were used as representatives of the

conditions during the operations of the San Mateo Landfill. The major parameters

measured were averaged to represent the year they were taken (Table 4, 5, and 6)

before comparing to the results gathered in this study. Bar graphs were used to display

their relationship.

Pena-Muralla (1995) suggested using Analysis of Variance (ANOVA) with a

confidence level of 95% to determine if there exist significant variations between the

parameters measured over location, (Tangtatco and Wong, 1999).

For physicochemical parameters, t-test was used on data gathered on March 6

and one-way ANOVA was used on data gathered on March 19 to determine any

significant variations on site location.

One-way ANOVA was also used to determine possible significant variations

in major parameters (COD and metal concentrations) on site location. For Site B

which was sampled twice (once for each dates), t-test was to determine possible

significant variations in time.

28

IV. RESULTS AND DISCUSSION

4.1 Minor Physicochemical Parameters

4.1.1 Water Temperature

On March 6, water temperature ranged from 30.7oC in Site A to 31.6

oC in Site

B (Appendix I). The t-test result shows that there is a significant difference in water

temperature between the two sites – Site B being warmer than Site A (Appendix I).

Figure 4. Mean Water Temperature Values Across Sites (March 6, 2016)

On March 19, water temperature ranged from 23.2oC in Site C to 32.4

oC in

Site B (Appendix I). The ANOVA results show that there exist significant differences

across the sites – Site C being the coolest and Site B being the warmest compared to

the other sites (Appendix I).

30.7

31.5

30

30.2

30.4

30.6

30.8

31

31.2

31.4

31.6

31.8

1

Wat

er T

emp

erat

ure

Site A

Site B

29

Figure 5. Mean Water Temperature Values Across Sites (March 19, 2016)

Since anthropogenic activities were not observed in Sites A, C, D, and E, and

waste discharge is not immediately observed in Site B, the only possible source of

heat is the sun, in which case, the time and the place affect the results. Since the

temperatures for Sites C, D, and E were taken in the morning and that Site C is under

the shade of the trees, these readings were not unexpected.

28.5

23.2

30.2 32.4

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

1

Wat

er T

emp

erat

ure

Site D

Site C

Site E

Site B

30

4.1.2 pH

On March 6, pH levels range from 7.30 in Site B to 7.99 in Site A (Appendix

I). The t-test result shows that there is a significant difference in pH levels between

the two sites – Site B being more acidic than Site A (Appendix I).

Figure 6. Mean pH Levels Across Sites (March 6, 2016)

On March 19, pH levels range from 6.92 in Site B to 7.51 in Site E (Appendix

I). The ANOVA results show that there exist significant differences across the sites –

Site C being the most acidic and Site E being the most basic among the other sites

(Appendix I).

7.88

7.37

6.80

7.00

7.20

7.40

7.60

7.80

8.00

8.20

1

pH

Site A

Site B

31

Figure 7. Mean pH Levels Across Sites (March 19, 2016)

All of the sites were within the standard ranges implemented by DAO 34-90

(6.5 – 8.5) and DAO 35-90 (6.5 – 9.0). This pH range is considered as the optimal

range for aquatic life to thrive, as such, deviating from this range would cause greater

stress for the organisms living in the area.

pH also determines the solubility and biological availability of chemical

constituents such as nutrients and heavy metals. The more acidic the water is, the

more soluble and bioavailable the heavy metals tend to be. Given that, it could be

expected that Site C would contain a higher concentration of heavy metals compared

to the other sites.

7.13

6.97

7.45

7.14

6.60

6.70

6.80

6.90

7.00

7.10

7.20

7.30

7.40

7.50

7.60

1

pH

Site D

Site C

Site E

Site B

32

4.1.2 Conductivity

On March 6, conductivity levels ranged from 1.10 umS/cm in Site A to 1.33

umS/cm in Site B (Appendix I). The t-test results show that there is a significant

difference between the conductivity levels between the two sites – Site B being more

conductive than Site A (Appendix I).

Figure 8. Mean Conductivity Levels Across Sites (March 6, 2016)

On March 19, conductivity levels ranged from 1.00 umS/cm in Site C to 1.29

umS/cm in Site B (Appendix I). The ANOVA results show that there exist significant

differences on conductivity levels across sites – Site B and Site C being more

conductive than the other sites (Appendix I).

1.10

1.33

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1

Co

nd

uct

ivit

y (u

mS/

cm)

Site A

Site B

33

Figure 9. Mean Conductivity Levels Across Sites (March 19, 2016)

High conductivity levels were found on Site B and Site C. Higher conductivity

levels imply higher ion concentrations in the water. These conductive ions can be

indicative of soluble metals found within the sites. As such, it can be inferred that

Sites B and C have high concentrations of metals.

1.04

1.27

1.01

1.29

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1

Co

nd

uct

ivit

y (u

mS/

cm)

Site D

Site C

Site E

Site B

34

4.1.3 Turbidity

On March 6, the turbidity levels ranged from 55 NTU on both sites A and B to

62 NTU on Site A (Appendix I). The t-test results also show that there is no

significant difference on the turbidity levels between the two sites (Appendix I).

Figure 10. Mean Turbidity Levels Across Sites (March 6, 2016)

On March 19, mean turbidity level ranged from 0 NTU in Site C to 99 NTU in

Site B. The ANOVA results also show that there are significant differences on the

turbidity levels across sites – Site C being less turbid than the other sites.

58 58

0

10

20

30

40

50

60

70

1

Turb

idit

y (N

TU)

Site A

Site B

35

Figure 11. Mean Turbidity Levels Across Sites (March 19, 2016)

Turbidity refers to the opaqueness of water caused by suspended colloids. Site

A, B, D, and E have recorded elevated turbidity readings which is indicative of the

presence of suspended materials. On the other hand, Site C has recorded a reading of

0 for turbidity. The clear stream found in Site C can be a result of the rehabilitation

program implemented in the San Mateo Landfill (Table 1).

86

0

99

77

0

20

40

60

80

100

120

1

Turb

idit

y (N

TU)

Site D

Site C

Site E

Site B

36

4.1.4 Salinity

On March 6, the salinity levels ranged from 0.04 %(m/v) in Site A to 0.06

%(m/v) in Site B (Appendix I). The t-test results cannot indicate the variations

between the sites as they produced no variations within hence, the p-value cannot be

calculated (Appendix I).

Figure 12. Mean Salinity Levels Across Sites (March 6, 2016)

On March 19, the salinity levels ranged from 0.04 in sites D and E to 0.05 in

sites B and C. ANOVA results show that there exist significant differences on the

salinity levels across sites – Sites B and C being more saline than the remaining sites.

0.04

0.06

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

1

Salin

ity

%(m

/v)

Site A

Site B

37

Figure 13. Mean Salinity Levels Across Sites (March 19, 2016)

Salinity refers to the concentration of all dissolved salts in a solution. Salinity

is related to both pH and conductivity. The lower the pH of the water, the more it is

capable to dissolve the salt. The higher the amount of dissolved salt in water, the

higher its conductivity level would be. These results are supposed to be expected

since sites B and C are more acidic and are more conductive than the other sites.

0.04

0.05

0.04

0.05

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

1

Salin

ity

%(m

/v)

Site D

Site C

Site E

Site B

38

4.1.5 Dissolved Oxygen

On March 6, mean DO values ranged from 6.68 mg/L in Site B to 7.73 mg/L

in Site A (Appendix I). The t-test results show that there is a significant difference in

DO levels between the two sites – Site A containing more dissolved oxygen than Site

B (Appendix I).

Figure 14. Mean DO Levels Across Sites (March 6, 2016)

On March 19, mean DO values ranged from 5.51 mg/L in Site D to 15.06

mg/L in Site E (Appendix I). The ANOVA results show that there exist significant

differences on DO levels across sites – Site C containing more dissolved oxygen than

the other sites (Appendix I).

7.73

6.68

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

1

Dis

solv

ed O

xyge

n (

mg/

L)

Site A

Site B

39

Figure 15. Mean DO Levels Across Sites (March 19, 2016)

DO levels are affected by both temperature and salinity. As temperature and

salinity decrease, the higher the DO level would be. It is true in Site E given that it has

the highest DO level and having both temperature and salinity measurements

relatively low. On the other hand, Site B has the second highest DO level while

having a higher temperature and salinity reading however. Another factor may have

influenced Site B to have this measurement.

With all that is said and done, all the sampling sites are in accordance with the

DAO 34-90 standard of 5.0 mg/L.

5.51 5.81

15.06

8.96

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

1

Dis

solv

ed O

xyge

n (

mg/

L)

Site D

Site C

Site E

Site B

40

4.2 Major Parameters

4.2.1 Chemical Oxygen Demand (COD)

COD values ranged from a minimum mean value of 63 mg/L in Site C to a

maximum mean value of 129 mg/L in Site B (Figure 17). The ANOVA results show

that there exist significant differences in COD values among the sampling sites

(Appendix II). The cause of variation can be pointed at Site C as the other sites are

closer in value. The order of magnitude in decreasing order was seen as follows: B, D,

E, and C.

From 2286 mg/L in 1999 (Table 4) to 63 mg/L (Figure 17) in 2016, there is an

observable decrease in the COD level in the leachate stream. The decrease in COD

levels in the leachate stream can be attributed to the rehabilitation program

implemented in the landfill (Table 1). A decrease in COD level implies a decrease in

organic activities within the landfill. Also, DAO 35-90 states that the maximum

allowable COD level for effluent discharge of old existing industry on Class C waters

should be at 150mg/L which means that Site C passes the standard.

On the other hand, from 14.46 mg/L (Table 4) to 101 mg/L (Figure 17)

upstream and from 89.12 mg/L (Table 4) to 120 mg/L (Figure 17) downstream, there

is an observable increase in COD levels within the Bosoboso River. This increase in

COD level implies an increase in organic activities within the river. This increase in

organic activities can be caused by a different source of pollution.

41

Figure 16. Sampling Sites with Corresponding Mean COD Readings

Figure 17. Mean COD Levels Across Sites

*values from Site A were not obtained

*duplicate samples for Site D instead of triplicates

120

63

101

129

0

20

40

60

80

100

120

140

160

1

CO

D (

mg/

L)

Site D

Site C

Site E

Site B

42

4.2.2 Metals

4.2.2.1 Copper (Cu)

T-test was applied to Site B since there are two sampling dates on that site

(Appendix III). The results show that there are no significant differences for the

copper concentrations acquired between the two sampling dates. The data gathered

from Site B were merged afterwards to continue with the analysis.

Mean copper values ranged from 1.144 ppm in Site A to 1.190 ppm in Site E

(Figure 19). The ANOVA results show that there are no significant differences in

copper concentration among sites (Appendix III). All of the sites exceeded the DAO

34-90 standard of 0.05 ppm of dissolved copper in class C waters.

From 0.0068 (Table 5) ppm to 1.190 ppm (Figure 19) in the leachate stream,

from 0.0540 ppm (Table 5) to 1.155 ppm upstream (Figure 19), and from 0.0566 ppm

(Table 5) to 1.173 ppm (Figure 19) downstream, there is an observable increase in

copper concentration within the leachate stream and within the Bosoboso River. This

increase in copper can be attributed to natural causation due to the reason that these

sites were far from anthropogenic activities and the area might be rich in copper.

43

Figure 18. Sampling Sites with Corresponding Mean Cu Concentrations

Figure 19. Mean Cu Concentration Values Across Sites

*Site B samples for March 6 and 19 were combined

*All sites from different dates were combined

1.144

1.173 1.155

1.190 1.179

1.000

1.050

1.100

1.150

1.200

1.250

1.300

1

Co

nce

ntr

atio

n (

pp

m)

Site A

Site D

Site C

Site E

Site B

44

4.2.2.2 Iron (Fe)

T-test was applied to Site B since there are two sampling dates on that site

(Appendix IV). The ANOVA results show that there are no significant differences for

the iron concentrations acquired between the two sampling dates. The data gathered

from Site B were merged afterwards to complete the data.

Mean iron values ranged from 2.002 ppm in Site E to 3.800 ppm in Site C.

The ANOVA results (Appendix IV) show that there are significant differences in iron

concentration among sites. Site C and Site B possessed a significantly higher iron

concentration when compared to the other sites. Excessive amounts of iron in Site B

can be attributed to either the naturally iron rich soil or the anthropogenic activities

(being near the road and community) within the vicinity. Unguarded disposal of

municipal wastes were observed within the area which may have contributed to the

increase of iron. On the other hand, excessive amounts of iron in Site C can be

attributed to the deposition of such metals in the river-bottom sediments which

constantly in interaction with the water in the leachate stream.

From 8.34 ppm to 3.800 ppm in the leachate stream, from 1.35 ppm to 2.002

ppm upstream, and from 1.40 ppm to 2.224 ppm downstream, there is an observable

increase in Fe concentration within the leachate stream and the Bosoboso River. This

increase in iron concentration can be attributed to the deposition of metals in the river-

bottom sediments due to the landfill’s previous actions or to natural causation as these

sites were far from any anthropogenic activities and the area might be rich in iron.

45

Figure 20. Sampling Sites with Corresponding Mean Fe Concentrations

Figure 21. Mean Fe Concentration Values Across Sites

*Site B samples for March 6 and 19 were combined

*All sites from different dates were combined

2.321 2.224

3.800

2.002

2.988

0.000

1.000

2.000

3.000

4.000

5.000

6.000

1

Co

nce

ntr

atio

n (

pp

m)

Site A

Site D

Site C

Site E

Site B

46

V. CONCLUSION AND RECOMMENDATION

5.1 Conclusion

1. Significant site variations were observed for the major parameters COD and Fe;

however, there were no significant variations observed for Cu. COD levels were

significantly low on leachate stream which is indicative of low organic activity;

however, there is still a sudden spike in COD levels from upstream to downstream

after passing the leachate stream. On the other hand, high COD levels were

observed on the other sites especially further upstream. The growing organic

activities on the Bosoboso River cannot be attributed to the leachate stream alone

based from this information.

2. High Fe concentrations were observed further upstream and in the leachate

stream. The high concentration found in the leachate stream can be attributed to

the lingering effects of the leachate on the waterway while the high concentration

found further upstream can be attributed to the community living near the area.

Also, Fe concentrations seem to slightly increase from upstream to downstream

after passing the leachate stream.

3. Aside from the leachate stream, all major parameters seem to have greatly

increased over time. It can be an implication of the further deteriorating quality of

Bosoboso River that is not caused by the San Mateo Landfill anymore.

5.2 Recommendation

Making this study temporal will improve the assessment of the Bosoboso

River’s current state. A monthly study can provide a better picture of the

physicochemical and biological processes in the river. A yearly study, on the other

47

hand, can provide a picture on the recovering process of the river. Sediment sampling

and macro-benthic assessments can also increase the assessment of the lingering

effects of the leachate on the river.

48

REFERENCES

News Articles

Canlas, J. (2005, December 19). San Mateo landfill ordered permanently banned

closed by SC. The Manila Times, p. A8. Retrieved from

https://news.google.com/newspapers?nid=2518&dat=20051219&id=l7ljAAAAIBAJ

&sjid=DSgMAAAAIBAJ&pg=1490,11863868&hl=en

Ubac, Mirahel. “Wawa Dam Polluted.” Philippine Daily Inquirer, 24 November 1997,

p24.

Villanueva, M. (2006, February 2). MMDA to press for re-opening of San Mateo

sanitary landfill.The Philippine Star. Retrieved from

http://www.philstar.com/metro/319635/mmda-press-re-opening-san-mateo-sanitary-

landfill

Gray Material

Tangtatco, Michael et al. 'Metals And Total Organic Levels In The San Mateo

Landfill Leachate And Its Receiving Water Systems'. Ateneo de Manila University

(1999). Thesis.

Tolentino, S. E., Jr. (2006). Authority to Close (Philippines, Department of

Environment and Natural Resources, Environmental Management Bureu). Metro

Manila, NCR: 6th Flr. DENR by the Bay Bldg.

Books

Pena-Muralla, Rosanna. Biostatistics. Quezon City: Office of Research and

Publications, Ateneo de Manila. 1995

Eaton, Andrew, Lenore Clesceri, and Arnold Greenberg, ed. Standard Methods. 19th

Ed. Washington D.C.:APHA. 1995

Web Pages

Chemical Oxygen Demand - Cod, Assay, Bod, and Waters. (2015). Retrieved from

http://science.jrank.org/pages/1388/Chemical-Oxygen-Demand.html

Copper - Element information, properties, and uses. (2015). Retrieved from

http://www.rsc.org/periodic-table/element/29/copper

Iron - Element information, properties, and uses. (2015). Retrieved from

http://www.rsc.org/periodic-table/element/26/iron

Kemker, C. (2014, March 03). Conductivity, Salinity and Total Dissolved Solids.

Retrieved from http://www.fondriest.com/environmental-

measurements/parameters/water-quality/conductivity-salinity-tds/

Kemker, C. (2013, November 19). Dissolved Oxygen. Retrieved from

http://www.fondriest.com/environmental-measurements/parameters/water-

quality/dissolved-oxygen/

49

Kemker, C. (2013, November 19). pH of Water. Retrieved from

http://www.fondriest.com/environmental-measurements/parameters/water-quality/ph/

Kemker, C. (2014, June 13). Turbidity, Total Suspended Solids and Water Clarity.

Retrieved from http://www.fondriest.com/environmental-

measurements/parameters/water-quality/turbidity-total-suspended-solids-water-

clarity/

Kemker, C. (2014, February 07). Water Temperature. Retrieved from

http://www.fondriest.com/environmental-measurements/parameters/water-

quality/water-temperature/

pH: Water properties. (2015). Retrieved from http://water.usgs.gov/edu/ph.html

Journal Article

Garbino, J. R., Hayes, H. C., Roth, D. A., Antweiler, R. C., Brinton, T. I., & Taylor,

H. E. (1995). Heavy Metals in the Mississippi River (R. H. Meade, Ed.). U.S.

GEOLOGICAL SURVEY CIRCULAR 1133. Retrieved from

http://pubs.usgs.gov/circ/circ1133/heavy-metals.html

50

Appendix I – Physicochemical Data and Detailed Results

Date: March 6, 2016

Site Temperature

pH Conductivity Turbidity Salinity DO

(oC) (umS/cm) (NTU) %(m/v) (mg /L)

A1 30.7 7.97 1.10 62 0.04 7.90

A2 30.7 7.99 1.10 56 0.04 7.60

A3 30.7 7.68 1.10 55 0.04 7.69

B1 31.6 7.50 1.33 55 0.06 6.90

B2 31.5 7.30 1.33 58 0.06 6.64

B3 31.5 7.31 1.32 61 0.06 6.51

Date: March19, 2016

Site Temperature

pH Conductivity Turbidity Salinity DO

(oC) (umS/cm) (NTU) %(m/v) (mg /L)

B1 32.3 7.12 1.29 85 0.06 8.86

B2 32.4 7.15 1.29 78 0.05 8.39

B3 32.4 7.15 1.29 68 0.05 9.64

C1 23.2 7.00 1.27 0 0.05 6.13

C2 23.2 6.99 1.27 0 0.05 6.22

C3 23.2 6.92 1.27 1 0.05 5.09

D1 28.5 6.95 1.04 97 0.04 2.43

D2 28.3 7.21 1.04 80 0.04 6.51

D3 28.7 7.23 1.05 80 0.04 7.58

E1 30.4 7.39 1.00 97 0.04 16.30

E2 29.9 7.46 1.01 98 0.04 15.00

E3 30.4 7.51 1.01 103 0.04 13.88

51

Date: March 6, 2016

Water Temperature

Hypothesis:

Ho: Water temperature values from Site A and B do not change significantly.

H1: Water temperature values from Site A and B do change significantly.

Solution:

t-Test: Two-Sample Assuming Unequal Variances

Site B Site A

Mean 31.53333 30.7

Variance 0.003333 0

Observations 3 3

Hypothesized Mean Difference 0 Df 2 t Stat 25 P(T<=t) one-tail 0.000798 t Critical one-tail 2.919986 P(T<=t) two-tail 0.001596 t Critical two-tail 4.302653

Conclusion:

Since the computed t Stat value is greater than the computed t Critical two-tail

value for site variations, Ho is rejected in favor of H1 with at least 95%

accuracy. There exists a significant difference in water temperature values

among the two sampling sites.

52

pH

Hypothesis:

Ho: pH values from Site A and B do not change significantly.

H1: pH values from Site A and B do change significantly.

Solution:


Site A Site B

Mean 7.88 7.37

Variance 0.0301 0.0127

Observations 3 3

Hypothesized Mean Difference 0 Df 3 t Stat 4.269814 P(T<=t) one-tail 0.011789 t Critical one-tail 2.353363 P(T<=t) two-tail 0.023578 t Critical two-tail 3.182446

Conclusion:



accuracy. There exists a significant difference in pH values among the two

sampling sites.

53

Conductivity

Hypothesis:

Ho: Conductivity values from Site A and B do not change significantly.

H1: Conductivity values from Site A and B do change significantly.

Solution:


Site B Site A

Mean 1.326667 1.1

Variance 3.33E-05 0

Observations 3 3

Hypothesized Mean Difference 0 Df 2 t Stat 68 P(T<=t) one-tail 0.000108 t Critical one-tail 2.919986 P(T<=t) two-tail 0.000216 t Critical two-tail 4.302653

Conclusion:



accuracy. There exists a significant difference in conductivity values among

the two sampling sites.

54

Turbidity

Hypothesis:

Ho: Turbidity values from Site A and B do not change significantly.

H1: Turbidity values from Site A and B do change significantly.

Solution:


Site B Site A

Mean 58 57.66667

Variance 9 14.33333

Observations 3 3


Conclusion:

Since the computed t Stat value is less than the computed t Critical two-tail

value for site variations, Ho is accepted. There is no significant difference in

turbidity values among the two sampling sites.

55

Salinity

Hypothesis:

Ho: Salinity values from Site A and B do not change significantly.

H1: Salinity values from Site A and B do change significantly.

Solution:


Site B Site A

Mean 0.06 0.04

Variance 0 0

Observations 3 3

Hypothesized Mean Difference 0 Df 65535 t Stat 65535 P(T<=t) one-tail #NUM! t Critical one-tail #NUM! P(T<=t) two-tail #NUM! t Critical two-tail #NUM!

Conclusion:

The p value and t Critical two-tail value cannot be computed due to variance

of both sample sets equating to 0. Therefore, the whole analysis is not

considered.

56

Dissolved Oxygen

Hypothesis:

Ho: DO values from Site A and B do not change significantly.

H1: DO values from Site A and B do change significantly.

Solution:


Site A Site B

Mean 7.73 6.683333

Variance 0.0237 0.039433

Observations 3 3


Conclusion:



accuracy. There exists a significant difference in DO values among the two

sampling sites.

57

Date: March 19, 2016

Water Temperature

Hypothesis:

Ho: Water temperature values across sites do not change significantly.

H1: Water temperature values across sites do change significantly.

Solution:

Anova: Single Factor

SUMMARY Groups Count Sum Average Variance

Site B 3 97.1 32.36666667 0.003333 Site C 3 69.6 23.2 0 Site D 3 85.5 28.5 0.04 Site E 3 90.7 30.23333333 0.083333

ANOVA Source of Variation SS df MS F P-value F crit

Between Groups 138.06917 3 46.02305556 1453.36 2.77E-11 4.066181

Within Groups 0.2533333 8 0.031666667

Total 138.3225 11

Conclusion:

Since the computed F value is greater than the computed F crit for site

variations, Ho is rejected in favor of H1 with at least 95% accuracy. There

exist significant differences in water temperature values among the sampling

sites.

58

pH

Hypothesis:

Ho: pH values across sites do not change significantly.

H1: pH values across sites do change significantly.

Solution:



Site B 3 21.42 7.14 0.0003 Site C 3 20.91 6.97 0.0019 Site D 3 21.39 7.13 0.0244 Site E 3 22.36 7.453333333 0.003633


Between Groups 0.3682 3 0.122733333 16.23815 0.000918 4.066181

Within Groups 0.0604667 8 0.007558333

Total 0.4286667 11

Conclusion:



exist significant differences in pH values among the sampling sites.

59

Conductivity

Hypothesis:

Ho: Conductivity values across sites do not change significantly.

H1: Conductivity values across sites do change significantly.

Solution:



Column 1 3 3.87 1.29 0 Column 2 3 3.81 1.27 0 Column 3 3 3.13 1.043333333 3.33E-05 Column 4 3 3.02 1.006666667 3.33E-05


Between Groups 0.1976917 3 0.065897222 3953.833 5.08E-13 4.066181

Within Groups 0.0001333 8 1.66667E-05

Total 0.197825 11

Conclusion:



exist significant differences in conductivity values among the sampling sites.

60

Turbidity

Hypothesis:

Ho: Turbidity values across sites do not change significantly.

H1: Turbidity values across sites do change significantly.

Solution:



Column 1 3 231 77 73 Column 2 3 1 0.333333333 0.333333 Column 3 3 257 85.66666667 96.33333 Column 4 3 298 99.33333333 10.33333


Between Groups 17790.917 3 5930.305556 131.7846 3.78E-07 4.066181

Within Groups 360 8 45

Total 18150.917 11

Conclusion:



exist significant differences in turbidity values among the sampling sites.

61

Salinity

Hypothesis:

Ho: Salinity values across sites do not change significantly.

H1: Salinity values across sites do change significantly.

Solution:



Column 1 3 0.16 0.053333333 3.33E-05 Column 2 3 0.15 0.05 7.22E-35 Column 3 3 0.12 0.04 0 Column 4 3 0.12 0.04 0


Between Groups 0.000425 3 0.000141667 17 0.000785 4.066181

Within Groups 6.667E-05 8 8.33333E-06

Total 0.0004917 11

Conclusion:



exist significant differences in salinity values among the sampling sites.

62

Dissolved Oxygen

Hypothesis:

Ho: DO values across sites do not change significantly.

H1: DO values across sites do change significantly.

Solution:



Column 1 3 26.89 8.963333333 0.398633 Column 2 3 17.44 5.813333333 0.394433 Column 3 3 16.52 5.506666667 7.385633 Column 4 3 45.18 15.06 1.4668


Between Groups 176.92609 3 58.97536389 24.45715 0.000221 4.066181

Within Groups 19.291 8 2.411375

Total 196.21709 11

Conclusion:



exist significant differences in DO values among the sampling sites.

63

Appendix II – COD Raw Data and Detailed Results


Sample COD Value (mg/L)

Site B 1 2 3 Average Total Average Standard Deviation

1 97 110 132 113 129 18.2

2 127 151 155 144

3 122 136 128 129

Site C 1 2 3 Average Total Average Standard Deviation

1 78 72 71 74 63 17.6

2 31 67 38 45

3 77 77 55 70

Site D 1 2 3 Average Total Average Standard Deviation

1 125 118 98 114 120 14.8

2 140 128 109 126

3 - - - -

Site E 1 2 3 Average Total Average Standard Deviation

1 125 118 98 114 101 24.6

2 129 110 110 116

3 56 70 95 74

64

Hypothesis:

Ho: COD values across sites do not change significantly.

H1: COD values across sites do change significantly.

Solution:



Site B 3 339 113 313 Site B 3 433 144.3 229.3 Site B 3 386 128.7 49.33 Site C 3 221 73.67 14.33 Site C 3 136 45.33 364.3 Site C 3 209 69.67 161.3 Site D 3 341 113.7 196.3 Site D 3 377 125.7 244.3 Site E 3 341 113.7 196.3 Site E 3 349 116.3 120.3 Site E 3 221 73.67 390.3

ANOVA Source of Variation SS Df MS F P-value F crit

Between Groups 28567.21212 10 2857 13.79 3E-07 2.297

Within Groups 4558.666667 22 207.2

Total 33125.87879 32

Conclusion:



exist significant differences in COD values among the sampling sites.

65

Appendix III – Cu Calibration Curve, Raw Data, and Detailed Results

Date: March 6, 2016

Sample Absorbance Concentration (ppm)

Site A 1 2 3 1 2 3 Average Standard Deviation

1 0.002 0.001 0.004 1.135 1.132 1.142 1.144 0.011760025

2 0.002 0.004 0.003 1.135 1.142 1.138

3 0.010 0.005 0.011 1.162 1.145 1.165

Site B Average Standard Deviation

1 0.018 0.120 0.009 1.168 1.527 1.158 1.198 0.12359159

2 0.008 0.011 0.010 1.155 1.165 1.162

3 0.004 0.009 0.007 1.142 1.158 1.152



Site B 1 2 3 1 2 3 Average Standard Deviation

1 0.012 0.012 0.010 1.168 1.168 1.162 1.159 0.01035654

2 0.010 0.013 0.003 1.162 1.172 1.138

3 0.007 0.008 0.008 1.152 1.155 1.155

Site C Average Standard Deviation

1 0.010 0.004 0.010 1.162 1.142 1.162 1.155 0.011334181

2 0.004 0.011 0.011 1.142 1.165 1.165

3 0.009 0.003 0.011 1.158 1.138 1.165

Site D Average Standard Deviation

1 0.008 0.006 0.014 1.155 1.148 1.175 1.173 0.021434955

2 0.012 0.004 0.018 1.168 1.142 1.188

3 0.019 0.019 0.022 1.191 1.191 1.201

Site E Average Standard Deviation

1 0.022 0.019 0.019 1.201 1.191 1.191 1.190 0.008641818

2 0.017 0.020 0.019 1.185 1.195 1.191

3 0.021 0.013 0.018 1.198 1.172 1.188

0 0.001

0.004

0.006

0.009

y = 0.0017x - 0.0002 R² = 0.969

00.0010.0020.0030.0040.0050.0060.0070.0080.009

0.01

0 1 2 3 4 5 6

Ab

sorb

ance

Concentration (ppm)

Calibration Curve (Cu)

66

Site B Date Comparison

Hypothesis

Ho: Cu concentrations between the two sampling dates do not change significantly.

H1: Cu concentrations between the two sampling dates do change significantly.

Solution:



B1 3 3.853778606 1.284592869 0.044 B1 3 3.481240021 1.16041334 3E-05 B1 3 3.451303885 1.150434628 7E-05 B2 3 3.497871208 1.165957069 1E-05 B2 3 3.471261309 1.157087103 3E-04 B2 3 3.461282597 1.153760866 4E-06

ANOVA Source of Variation SS Df MS F P-value F crit

Between Groups 0.040794 5 0.008158855 1.096 0.411 3.105875239

Within Groups 0.0893 12 0.007441672

Total 0.130094 17

Conclusion:

Since the computed F value is lesser than the computed F crit for time

variations, Ho is accepted. There is no significant difference in Cu

concentrations between the two sampling dates.

67

Site Comparison

Hypothesis

Ho: Cu concentrations across sites do not change significantly.

H1: Cu concentrations across sites do change significantly.

Solution:



A 3 3.408062799 1.14 2.58157E-05 A 3 3.414715274 1.14 1.10639E-05 A 3 3.471261309 1.16 0.000114327 B 3 3.853778606 1.28 0.044244356 B 3 3.481240021 1.16 2.58157E-05 B 3 3.451303885 1.15 7.00711E-05 B 3 3.497871208 1.17 1.47518E-05 B 3 3.471261309 1.16 0.000291348 B 3 3.461282597 1.15 3.68795E-06 C 3 3.464608834 1.15 0.000132766 C 3 3.471261309 1.16 0.00018071 C 3 3.461282597 1.15 0.000191773 D 3 3.477913784 1.16 0.000191773 D 3 3.497871208 1.17 0.000545817 D 3 3.584353379 1.19 3.31916E-05 E 3 3.584353379 1.19 3.31916E-05 E 3 3.57104843 1.19 2.58157E-05 E 3 3.557743481 1.19 0.00018071


Between Groups 0.057207506 17 0 1.307784902 0.242641534 1.92

Within Groups 0.09263397 36 0

Total 0.149841476 53

Conclusion:

Since the computed F value is lesser than the computed F crit for site

variations, Ho is accepted. There exist no significant differences in Cu

concentrations among the sampling sites.

68

Appendix IV – Fe Calibration Curve, Raw Data, and Detailed Results

Date: March 6, 2016


Site A 1 2 3 1 2 3 Average Standard Deviation

1 0.250 0.723 0.212 1.960 3.533 1.833 2.321 0.609979495

2 0.417 0.265 0.297 2.515 2.010 2.116

3 0.503 0.116 0.444 2.801 1.514 2.605

Site B Average Standard Deviation

1 0.300 0.895 0.542 2.126 4.105 2.931 3.065 0.847383041

2 0.753 0.977 0.659 3.633 4.378 3.320

3 0.281 0.373 0.461 2.063 2.369 2.662



Site B 1 2 3 1 2 3 Average Standard Deviation

1 0.462 0.207 0.722 2.665 1.817 3.530 2.863 0.780777295

2 0.743 0.801 0.211 3.600 3.793 1.830

3 0.430 0.381 0.737 2.559 2.396 3.580

Site C Average Standard Deviation

1 1.562 1.161 1.599 6.324 4.990 6.447 3.800 1.808806106

2 0.279 0.529 0.524 2.056 2.888 2.871

3 1.007 0.381 0.186 4.478 2.396 1.747

Site D Average Standard Deviation

1 0.570 0.575 0.232 3.024 3.041 1.900 2.224 0.604621098

2 0.403 0.321 0.083 2.469 2.196 1.404

3 0.452 0.139 0.191 2.632 1.591 1.764

Site E Average Standard Deviation

1 0.369 0.523 0.106 2.356 2.868 1.481 2.002 0.491138717

2 0.224 0.258 0.155 1.873 1.986 1.644

3 0.108 0.431 0.189 1.487 2.562 1.757

0.171 0.853

2.474

3.449

6.389

y = 0.3006x - 0.3392 R² = 0.9406

0

1

2

3

4

5

6

7

0 5 10 15 20 25

Ab

sorb

ance

Concentration (ppm)

Calibration Curve (Fe)

69

Site B Date Comparison

Hypothesis

Ho: Fe concentrations between the two sampling dates do not change significantly.

H1: Fe concentrations between the two sampling dates do change significantly.

Solution:



B1 3 9.162453 3.0541511 0.9905801 B1 3 11.33116 3.7770534 0.2952869 B1 3 7.093534 2.3645113 0.089632 B2 3 8.011575 2.6705251 0.7336258 B2 3 9.222326 3.0741086 1.1699805 B2 3 8.533795 2.8445982 0.4119184


Between Groups 3.4232336 5 0.6846467 1.1129379 0.40375 3.105875239

Within Groups 7.3820474 12 0.6151706

Total 10.805281 17

Conclusion:

Since the computed F value is lesser than the computed F crit for time

variations, Ho is accepted. There is no significant difference in Fe

concentrations between the two sampling dates.

70

Site Comparison

Hypothesis

Ho: Fe concentrations across sites do not change significantly.

H1: Fe concentrations across sites do change significantly.

Solution:



A 3 7.32637 2.442123 0.896714 A 3 6.641166 2.213722 0.071045 A 3 6.920569 2.306856 0.480972 B 3 9.162453 3.054151 0.99058 B 3 11.33116 3.777053 0.295287 B 3 7.093534 2.364511 0.089632 B 3 8.011575 2.670525 0.733626 B 3 9.222326 3.074109 1.169981 B 3 8.533795 2.844598 0.411918 C 3 17.76078 5.920259 0.652793 C 3 7.815327 2.605109 0.225979 C 3 8.620277 2.873426 2.035642 D 3 7.965008 2.655003 0.427651 D 3 6.069053 2.023018 0.305672 D 3 5.985897 1.995299 0.311252 E 3 6.704364 2.234788 0.491925 E 3 5.503592 1.834531 0.030474 E 3 5.80628 1.935427 0.312469


Between Groups 44.79179 17 2.634811 4.774356 4E-05 1.915321

Within Groups 19.86722 36 0.551867

Total 64.65901 53

Conclusion:



exist significant differences in Fe concentrations among the sampling sites.

71

Appendix V – Laboratory Procedures

COD Analysis - Method 8000 (Reactor Digestion Method)

1. Materials

2. Chemical Materials

a. COD Digestion Reagent Vial:

a. Low Range, 0 to 150 mg/L COD

b. High Range, 0 to 1500 mg/L COD

c. High Range Plus, 0 to 15000 mg/L COD

b. Deionized Water

3. Equipment

a. HACH DR/890 portable colorimeter

b. DRB 200 Reactor

c. COD/TNT Adapter

d. TenSette Pipet

e. Pipet Tips

f. Test Tube Rack

4. Procedure

A. Digestion

1. Homogenize 500 mL of samples for 2 minutes in a blender.

Note: For the 0-15000 mg/L range, homogenize 100mL of sample. Pour the

blended sample into a 250-mL beaker. Stir with magnetic stirrer while

withdrawing a sample aliquot. This improves accuracy and reproducibility.

2. Turn on the DRB 200 Reactor. Preheat to 150oC.

Note: See DRB 200 user manual for selecting pre-programmed temperature

applications.

3. Remove the cap of a COD Digestion Reagent Vial for the appropriate range:

Sample Concentration Range

(mg/L)

COD Digestion Reagent Vial

Type

0 – 150 Low Range

0 – 1500 High Range

0 – 15000 High Range Plus

Note: The reagent mixture is light-sensitive. Keep unused vials in the opaque

shipping container; in a refrigerator if possible. The light striking the vials

during the test will not affect results.

4. Hold the vial at a 45-degree angle. Pipet 2.00 mL (0.2 mL for the 0 to 15000

mg/L range) of sample into the vial.

Note: For the 0-15000 mg/L range, pipet only 0.20 mL of sample, not 2.00mL

of sample, using a TenSette Pipet. For greater accuracy analyze a minimum of

three replicates and average the results.

72

Note: Spilled reagents will affect test accuracy and is hazardous to skin and

other materials. Do not run tests with vials which have been spilled. If spills

occur, wash with running water.

5. Replace the vial cap tightly. Rinse the outside of the COD vial with deionized

water and wipe the vial clean with a paper towel.

6. Hold the vial by the cap and over a sink. Invert gently several times to mix

contents. Place the vial in the preheated DRB 200 Reactor.

Note: The vial will become very hot during mixing.

7. Prepare a blank by repeating steps 3 to 6. Substituting 2.00 mL of (0.2mL for

the 0 to 15000 mg/L range) deionized water for the sample.

Note: Be sure the pipet is clean.

Note: One blank must be run with each of samples. Run samples and blanks

with vials from the same lot number (lot # is on the container label).

8. Heat the vials for two hours.

Note: Many samples are digested completely in less than two hours. If desired,

measure the concentration (while still hot) at 15 minute intervals until the

reading remains unchanged. Cool vials to room temperature for final

measurement.

9. Turn the reactor off. Wait about 20 minutes for the vials to cool to 120oC or

less.

10. Invert each vial several times while still warm. Place the vials into a rack.

Wait until the vials have cooled to room temperature.

11. Use one of the analytical techniques to measure the COD:

Colorimetric method, 0 -150 mg/L COD



B. Colorimetric Determination

1. Entered the stored program number for chemical oxygen demand (COD) high

range.

Press: PRGM

The display will show: PRGM ?

2. Press: 17 PRGM

The display will show mg/L, COD and the ZERO icon.

Note: For alternate form (O2), press the CONC key.

3. Insert the COD/TNT Adapter into the cell holder by rotating the adapter until

it drops into place. Then push down to fully insert it.

73

4. Clean the outside of the blank with a towel.

Note: Wiping with a damp towel followed by a dry one will remove

fingerprints and other marks.

5. Place the blank in the adapter.

Push straight down on the top of the vial until it seats solidly into the adapter.

Note: Do not move the vial from side to side as this can cause errors.

6. Tightly cover the sample cell with the instrument cap.

The blank is stable when stored in the dark. See Blanks for Colorimetric

Determination following these procedures.

7. Press: ZERO

The cursor will move to the right, then the display will show: 0 mg/L COD

8. Clean the outside of the sample vial with a towel.

9. Place the sample in the adapter.

Push straight down on the top of the vial until it seats solidly into the adapter.

Note: Do not move the vial from side to side as this can cause errors.

10. Tightly cover the sample cell with the instrument cap.

11. Press: READ

The cursor will move to the right, then the result in mg/L COD will be

displayed.

Note: When using High Range Plus COD Digestion Vials, multiply the

reading by 10.

Note: For most accurate results with samples near 1500 or 15000 mg/L COD,

repeat the analysis with a diluted sample.

74

Metals Analysis

1. Materials

a. 67 plastic sample bottles

b. 2 - 10 ml pipettes

c. 100 ml graduated cylinder

d. Filter paper

e. Wash bottle

f. 2 funnels

g. 6 - 100 ml Volumetric flasks

h. Aspirator

i. Plastic goggles

2. Chemical Materials

a. Cu stock solution

b. Fe stock solution

c. Zn stock solution

d. Concentrated Nitric acid

e. Concentrated Hydrochloric acid

3. Equipment

a. Atomic Absorption Spectroscopy (AAS)

b. Hotplate

c. Hood with glass shield

4. Procedure

A. Preparation of Stock Solutions

1. Preparation of Fe Standard Solutions.

a. From the standard 100 ppm Fe solution, prepare the dilutions needed to

form the appropriate standards for the calibration curve.

2. Preparation of Cu Standard Solutions

a. From the standard 100 ppm Fe solution, prepare the dilutions needed to

perform the appropriate standards for the calibration curve.

3. Preparation of Zn Standard Solutions

a. From the standard 100 ppm Zn solution, prepare the dilutions needed to

form the appropriate standards for the calibration curve.

B. Nitric Acid Digestion of Samples

1. Measure 100 ml of sample using a 100ml graduated cylinder and transfer the

sample to a beaker, cover the top with watch glass, and set aside for a while.

Pipette 5 ml concentrated nitric acid and 2 ml of hydrochloric acid. Do this to

the other replicates.

2. Use a hot plate to simultaneously bring the samples to a slow boil. Evaporate

up to the lowest volume possible (about 10 to 20 ml).

3. Digestion is complete when a light-colored, clear solution is seen. Do not let

the sample dry during digestion.

4. When digestion is complete, wash down the walls of the flask using distilled

water. Then, filter the liquid. Do this by running the liquid into a funnel

covered with filter paper, and transfer the filtrate into a 100 ml volumetric

flask. Make at least two distilled water rinsing of both the beaker and funnel.

Add these washings into the volumetric flask, and dilute with distilled water

up to the mark.

C. Analysis of Samples using a Calibration Curve

1. Aspirate the appropriate standards into the AAS.

75

2. Form a calibration curve by correlating metal concentration with absorbance.

3. Aspirate the digested samples into the flame.

4. Determine the metal concentration.

Experimental

Condition Fe Cu

Matrix Aqueous Aqueous

Lamp Current (mA) 7.0 4.0

Wavelength (nm) 248.3 324.7

Slit Width (nm) 0.2 0.5

thesis manuscript_the end

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