i

ADABA .C. SANDRA

PG/ M.ENG/2009/ 50686

PG/M. Sc/09/51723

EFFECTS OF PARTICLES SIZES BIOREMEDIATION OF CRUDE ON POLLUTED

SANDY SOILS

CIVIL ENGINEERING

A THESIS SUBMITTED TO THE DEPARTMENT OF CIVIL ENGINEERING, FACULTY

OFENGINEERING, UNIVERSITY OF NIGERIA, NSUKKA

Webmaster

Digitally Signed by Webmaster’s Name

DN : CN = Webmaster’s name O= University of Nigeria, Nsukka

OU = Innovation Centre

JANUARY, 2011

ii

EFFECTS OF PARTICLE SIZES ON BIOREMEDIATION OF CRUDE OIL POLLUTED SANDY SOILS

BY

ADABA .C. SANDRA

PG/ M.ENG/2009/ 50686

A PROJECT SUBMITTED IN PARTIAL FULFILLMENT FOR THE REQUIREMENT OF THE AWARD OF MASTERS OF

ENGINEERING (M.ENG) IN ENVIRONMENTAL AND WATER RESOURCE ENGINEERING

TO

DEPARTMENT OF CIVIL ENGINEERING,

FALCULTY OF ENGINEERING,

UNIVERSITY OF NIGERIA, NSUKKA

JANUARY, 2011.

i

TITLE PAGE

EFFECTS OF PARTICLE SIZES ON BIOREMEDIATION OF CRUDE OIL

POLLUTED SANDY SOILS

ii

CERTIFICATION

Adaba Chibuzor Sandra, a postgraduate student in the Department of Civil

Engineering with Reg. No. PG/M.ENG/09/50686 has satisfactorily completed the

requirement for the research work for the degree of Master of Engineering in Civil

Engineering. The work embodied in this thesis is original and has not been submitted

in full for any other diploma or degree of this or any other University

...................................

Adaba Chibuzor Sandra

(Student)

..... .................................... ...............................................

Engr. Prof. J.C Agunwamba Engr. J.C. Ezeokonkwo

(SUPERVISOR) (HEAD OF DEPARTMENT)

............................................... ................................................

DEAN, FACULTY OF ENGINEERING EXTERNAL EXAMINER

iii

DEDICATION

This work is dedicated to the Almighty God my source of inspiration

and

My Family

iv

ACKNOWLEDGEMENT

I am greatly indebted to my supervisor Engr Prof.J.C Agunwamba whose fatherly

comments ,direction and encouragement saw me through this work.

Not forgotten is Mr Desmond Ewa for his intellectual contributions and all the staff of

Civil Engineering Department, UNN especially my lecturers, staff of Civil

Engineering Workshop, staff of Searchgate laboratory, who were very helpful during

my sustained and tedious experimental work.

With much gratitude I acknowledge Engr Fabian Allanah and Agbiji Ndifon for their

financial assistance ,my colleagues and friend especially Akano Jason.

Not the least, are all the people whose names are not mentioned at this point and I

hope they will accept my appreciation for their different contributions.

Finally, my appreciation goes to all the members of Adaba‘s family for their prayers

and encouragement.

Adaba C.S

Civil Eng. Department

U.N.N

v

TABLE OF CONTENTS

TITLE PAGE i

CERTIFICATION PAGE ii

DEDICATION iii

ACKNOWLEDGEMENT iv

TABLE OF CONTENTS v

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF ABBREVIATIONS xi

ABSTRACT xiii

CHAPTER ONE

1.0 Introduction 1

1.1 Background of Study 1

1.2 Statement of Problem 3

1.3 Significance of Study 3

1.4 Objective of Study 3

1.5 Scope of Study 4

1.6 Limitation 4

CHAPTER TWO

2.0 Literature Review 5

2.1 Cohessionless Soils 5

2.2 Particle Size Distribution and Index Properties 5

2.2.1 Sand Percentage 5

2.2.2 Soil Colour 6

2.2.3 Soil Texture 6

2.2.4 Soil Aggregate and Structure 6

2.3 Effects of Petroleum Spill on the Environment 7

vi

2.3.1 Effect on Plants 7

2.3.2 Effects on the Geotechnical Properties of Soils 7

2.3.3 Effects on Sandy Soils 8

2.4 Concept of Bioremediation and History 9

2.4.1 Biological Process of Bioremediation 10

2.4.2 Petroleum Hydrocarbon Degrading Microorganisms 10

2.4.3 How Bacteria Functions and Adapt to Environmental Conditions 12

2.4.4 Bacteria Reproduction and Survival 13

2.5 Growth Cycle of Microorganisms and Reproduction 13

2.5.1 Lag Phase 13

2.5.2 Stationary Phase 13

2.5.3 Exponential and Declining Phase 14

2.5.4 Death Phase 14

2.5.5 Kinetics of Bacteria Growth 15

2.6 Factors Influencing Hydrocarbon Metabolism on Bioremediation 15

2.6.1 Temperature and Chemical Composition of Crude Oil 16

2.6.2 Nutrients of Nitrogen and Phosphorus 16

2.6.3 Oxygen Requirement 17

2.6.4 Moisture Content and Surface Area 17

2.6.5 Soil pH 18

2.6.6 Organic Matter and Carbon 19

2.7 Techniques of Bioremediation 20

2.7.1 Bioaugmentation Process 20

2.7.2 Biostimulation Process 20

2.8 Importance and Advantages of Bioremediation 21

2.8.1 Disadvantages of Bioremediation 21

vii

CHAPTER THREE

3.0 Materials and Methods 22

3.1 Sample Collection 22

3.2 Experimental Procedures 23

3.2.1 Physical Parameters 23

3.3 Classification Test 24

3.3.1 Visual Description of Soil Colour and Structure 24

3.3.2 Sieve Analysis 24

3.3.3 Soil pH in Distilled Water 25

3.3.4 Total Hydrocarbon Content 25

3.3.5 Available Nitrogen Using H2O2/KCL Extraction 25

3.3.6 Available Phosphorus 25

3.3.7 Total Organic Carbon 26

3.3.8 Total Heterotrophic Bacteria Counts 26

3.3.9 Total Heterotrophic Fungi Count 26

3.3.10 Statistical Analysis 26

3.4 Equations for the Calculations of Parameters 26

3.4.1 Moisture Content 26

3.4.2 Particle Size Distribution 27

3.4.3 Soil pH in Water 27

3.4.4 Total Hydrocarbon Content 27

3.4.5 Total Organic Carbon Content 28

3.4.6 Available Phosphorus 28

3.4.7 Available Nitrogen 28

3.4.8 Total Heterotrophic Bacteria and Fungi Count 28

3.5 Statistical Analysis 29

viii

CHAPTER FOUR

4.0 Result and Discussion 30

4.1 Particle Size Distribution Parameters 30

4.2 Moisture Content 30

4.3 Total Hydrocarbon Content 31

4.4 Total Organic Carbon 31

4.5 Soil pH 31

4.6 Available Nitrogen 32

4.7 Available Phosphorus 32

4.8 Total Heterotrophic Bacteria and Fungi Count 32

4.9 Graphs 34

CHAPTER FIVE

5.0 Conclusion 46

5.1 Recommendation 46

REFERENCES 48

APPENDICES 53

ix

LIST OF TABLES

Table 2.0 : The Biodegradability of Different Petroleum Products

Table 2.1 : Microorganisms Capable of Degrading Petroleum Hydrocarbon

Table 2.2 : Class of Phosphorus

Table 2.3 : Soil Reaction pH Class Range

Table 2.4 : Class of Organic Carbons

x

LIST OF FIGURES

Fig 2.1 Growth Curve

Fig 4.1A: Particle Size Distribution for fine to coarse sand(X)

Fig.4. 1B: Particle Size Distribution for very fine to coarse sand (Y)

Fig 4.1C1: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs Cu for fine

to coarse sand(X)

Fig 4.1C2: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs Cu for very

fine to coarse sand(Y)

Fig 4.1 D1: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs D50 for fine

to coarse sand(X)

Fig 4.1 D2: Concentration of Remaining Crude Oil (mg/kg) Vs D50 at last Day for

very fine to coarse sand(Y)

Fig 4. 2A: Moisture Content Vs days for fine to coarse sand( X)

Fig 4.2B: Moisture Content Vs days for very fine to coarse sand(Y)

Fig 4.3A: Total Hydrocarbon Content Vs Days for fine to coarse sand(X)

Fig 4 .3B : Total Hydrocarbon Content Vs Days for very fine to coarse sand(Y)

Fig.4. 4A: Total Organic Carbon Vs days for fine to coarse sand(X)

Fig4. 4B: Total Organic Carbon Vs days for very fine to coarse sand (Y)

Fig 4.5A: Available Nitrogen Vs days for fine to coarse sand(X)

Fig 4.5B: Available Nitrogen Vs days for very fine to coarse sand(Y)

Fig 4. 6A: Available Phosphorus Vs days for fine to coarse sand(X)

Fig 4. 6B: Available Phosphorous Vs days for very fine to coarse sand(Y)

Fig 4.7A: pH Vs Days for fine to coarse sand(X)

Fig 4.7B: pH Vs Days for very fine to coarse sand(Y)

xi

Fig4. 8A: Total Heterotrophic Bacteria count cfux105/g Vs Time (days) for fine to

coarse sand(X)

Fig. 4.8B: Total Heterotrophic Bacteria Count cfu x105/g Vs days for very fine to

coarse sand(Y)

Fig 4.9A: Total Heterotrophic Fungi Counts cfux105/g Vs days for fine to coarse

sand(X)

Fig 4.9B: Total Heterotrophic Fungi Counts cfu x105/g Vs Days for very fine to

coarse sand (Y)

Fig 4. 10A: Rate of Hydrocarbon Loss (% Reduction Vs Days for fine to coarse

sand(X)

Fig 4.10B: Rate of Hydrocarbon Loss (% Reduction) Vs Days for very fine to coarse

sand (Y.)

xii

LIST OF ABBREVIATIONS AND NOTATIONS

AN Available Nitrogen

AP Available Phosphorus

CU Coefficient of Uniformity

CFU Colony Forming Unit

HUB Hydrocarbon Utilizing Bacteria

MC Moisture Content

THC Total Hydrocarbon Content

THBC Total Heterotrophic Bacteria Count

THFC Total Heterotrophic Fungi Count

TOC Total Organic Carbon

D50 Average Grain size

D10 Effective Size Diameter

R Correlation Coefficient

ND Not Detected

Vs Versus

% Percent

NPK Nitrogen, Phosphorus and Potassium

xiii

Abstract Bioremediation has been proven to be the most effective method of cleaning up oil

contaminated soils through the application of nutrients and microorganism to contaminated

soils. Hence, this research was aimed at investigating the effects of particle sizes on

bioremediation of crude oil polluted sandy soils. Six different soil samples were sieved using

the B.S sieve sizes. The sieve sizes were classified into X and Y such that X is fine to coarse

sand while Y is very fine to coarse sand according to U.S Bureau and PRA (Public Roads

Administration) soil classification system. The soil samples were polluted with escravous

sweet crude oil at a uniform rate of concentration under aerobic condition. Treatment

commenced after four days using nutrients and microorganism. Soil samples were examined

for physiochemical and microbial characteristics for a period of 42days. The parameters

examined were: moisture content, particle size distribution, total hydrocarbon content, soil

pH, available nitrogen, available phosphorus, total heterotrophic bacteria and fungi count.

The analysis of the soil characteristics throughout the remediation period showed that total

heterotrophic bacteria and fungi counts increased in all the soil samples. THBC was highest

in sample G for both fine to coarse sand(X) and very fine to coarse sand (Y ) with values of

250cfux105/g and 298 cfux10

5/g at least values of Cu and D50 respectively. There was a

decrease in nitrogen, phosphorus, organic carbon content, moisture content, pH and total

hydrocarbon content. The result of the study revealed that, the rate of hydrocarbon loss was

higher in samples with less Cu and D50 values compared to samples of higher values, an

indication that particle size distribution parameters could be one of the factors affecting

bioremediation. The correlation coefficient(r) of THC versus Cu for fine to coarse sand(X) is

0.867 while for very fine to coarse sand is 0.923.

1

CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF THE STUDY

In Nigeria, particularly in the Niger Delta regions; the soils found are mostly sandy soils in

shades of different colours of white, brown, grey and red. Sands are cohessionless aggregate

of rounded, subangular or angular fragments of more or less unaltered rock or mineral

particles of size from 0.075 -4.75mm (Murthy,2009). The sand separates recognized are: very

coarse, coarse medium, fine and very fine determined from the particle size distribution

curve. The coefficient of uniformity which is an index value showing the average slope of

grain size distribution in a soil depends on the gradation or distribution curve of the soil

sample. According to Arora (2008), the larger the numerical value of coefficient of

uniformity, the more the range of particles. Sand particles because of their size have a direct

impact on the porosity of the soil.

The high incidence and frequency of crude oil spill have been of great concern to

Environmental Engineers in Nigeria. This has given rise to intensive research to find ways

and means of generating information and data required to assist in bioremediation strategies

of crude oil spills.

Before any remediation strategy can be done successfully, a lot of information would be

required to aid the process (Bidemi, 2011). This information is meant to assist in the

detection of and response to oil spill incidence.

Bioremediation is a means of cleaning up contaminated environments by exploiting the

diverse metabolic abilities of microorganisms to convert contaminants to harmless products

by mineralization, generation of carbon (IV) oxide and water, or by conversion into microbial

2

biomass (Baggott, 1993; Mentzer and Ebere, 1996). In Nigeria, no information is yet

available regarding the commercial production of fungi or microbial inocula for use in

bioremediation of oil polluted environments.

The effectiveness of bioremediation is dependent upon physical and chemical condition as

well as correct analysis of the parent microbial population and environmental condition

(Nedwell, 1999). It has been found that oil is degraded efficiently by oil oxidizing

microorganism under laboratory and field condition (Grondeva et al., 1993). To enhance the

natural cleaning action, special fertilizer which contains nutrients of nitrogen, phosphorus and

potassium (NPK 15:15:15 fertilizer) is applied to the polluted site. Bioaugmentation process

of bioremediation may not be effective for use in oil spill cleanup situation because the

addition of non native organisms will often cause competition with the existing beneficial

microorganisms (Zhu, etal., 2001).

Some of the naturally occurring microbes capable of degrading petroleum hydrocarbon are

Pseudomonas, achrombacter, arthrobacter, bacillus, flavobacter, nocardia, vibrio,

connybacterium, alcaligeu (all are bacteria organism).Yeast and fungi organisms are

Aspergilium, candida, cladspotum, penicillum, rhodomia, trichodermia (Zhu, et al., 2001).

Analysis of biodegradation rate of crude oil contaminated soil using fertilizer or cow dung

showed that fertilizer was a better nutrient source for biostimulation than cow dung

(Obahiagbon and Audu 2000). Numerous laboratory studies on nutrient enhancement of oil

degradation by natural occurring microorganism have concluded that, this technique is

promising for use in stimulating oil degradation (Amanchukwu etal., 1989, Pitchard and

Coastal 1991, Oliver etal, 1978). Excessive application of the fertilizer can lead to

accumulation of nutrients in the soil. The uncertainty about the toxicity of various fertilizer

formulations and microbial products inhibit broader use of bioremediation on marine

3

shorelines (Hoff, 1993). Field application of nutrients is influenced by temperature, water

runoff, substrate and other environmental parameters that are neither fully understood nor

easily quantified (Atlas, 1995).

The 1990 Gulf of Mexico spills clearly showed that bioremediation could not be measured in

minutes or hours but over a period of days and weeks (Hoff, etal., 1993). The Puerto Rico

spill of 1994 clearly showed that at warmer temperatures, bioremediation generally takes 6

weeks while at cooler temperature it spans to several months. This simply implies that

bioremediation is not a fast process but a slow process. It has been found that addition of

certain nutrients and microorganisms to crude oil contaminated soils fastens the rate of

hydrocarbon loss a process called bioremediation. Obahiagbon and Audu( 2000) in their

various researches have carried out extensive study of biodegradation rate of crude oil

contaminated soil using fertilizer or cow dung and observed that, fertilizer was a better

nutrient source for biostimulation than cow dung. Ayotamuno and Kogbara(2006) in their

study found out that, crude oil contamination of agricultural soils limits the availability of

oxygen in the soil layers and hence impedes the biodegradation process but they failed to

investigate on the particle sizes of the soil layers to detect the porosity and voids.

Other notable researchers have also carried out studies on the physiochemical and microbial

characteristic of various soils, but not much has been done on the effects of particle size on

bioremediation. With this as the study background, the physical properties, chemical and

microbial characteristics of the soil samples were used in the assessment of the rate of

Hydrocarbon loss at the end of remediation.

4

1.2 STATEMENT OF PROBLEM

There are very few information about the soil particle size properties on bioremediation.

Therefore, this research is aimed at investigating on the effects of particle size distribution

parameters like; effective size diameter (d10), coefficient of uniformity (Cu) and average grain

size (d50) as well as chemical and microbial properties of different sandy soils and their

effects on bioremediation

1.3 SIGNIFICANCE OF STUDY

Since bioremediation process of cleaning up oil spills has proven to be an effective method

but a slow process, this study is considered very important as it provides information and data

about the particle sizes of sandy soils (Cu and D50) on bioremediation. Through intensive

laboratory analysis, data generated will be used as reference tool for further research on

bioremediation, academic guide to students, Engineers, contractors and consultants who wish

to embark on a similar project.

Statistical Method using regression analysis was used at the end of the research to show the

linear relationship and correlation coefficient of the parameters with time in days.

1.4 OBJECTIVE OF STUDY

The objectives of the study is summarised thus:

Compare the effects of particle size distribution parameters (Cu and D50) on

bioremediation, characterise the soil and crude oil samples used for the experiment.

To determine the soil Physiochemical and microbial characteristics and their effects

on bioremediation.

5

Make comparison and discuss bioremediation results of soil samples with past related

projects.

1.5 SCOPE OF STUDY

1) Review of past literature on similar project

2) Sample collection and locations

3) Contamination of different sandy soil samples with crude oil at a uniform concentration of

4% each.

4) Laboratory analysis on Physical, Chemical and Microbial properties of the Soil samples.

5) Combined practice of bioaugmentation and biostimulation process of remediation

respectively.

6) Weekly soil sampling for laboratory analysis (0-7) days for a period of 42days.

7) Presentation and comparison of results with past related projects.

1.6 LIMITATION

Due to the different locations where the samples were got from, large quantity of disturbed

sandy soil samples required for the experimental work, were transported to the Civil

Engineering Laboratory of the University of Nigeria, Nsukka, this was tedious and expensive.

The non availability of required equipment, reagent and apparatus for the chemical and

microbial analysis of the soil resorted to; conducting the test in a Standard Laboratory

(Search Gate Laboratory Lagos, Soil Science and Microbiology Department UNN) which

was capital intensive and tedious. Preserving and transporting the samples particularly during

weekly sampling for 42days was tedious.

6

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 COHESIONLESS SOILS

Soils composed of bulky grains are cohesionless regardless of the fineness of the particles.

Sands are cohessionless coarse grained soil having particle sizes between 0.075mm to

4.75mm. They are visible to the naked eyes, granular, gritty and hardly exhibit plasticity and

cohesion (Obioha, 2001). According to, Murthy (2009), sand are cohessionless aggregates of

rounded, subangular or angular fragments of more or less unaltered rocks or mineral particles

of size from 0.075-4.75mm containing quartz minerals.

2.2 PARTICLE SIZE DISTRIBUTION AND INDEX PROPERTIES

Particle size is the effective diameter of a particle as measured by sedimentation, sieving, or

micrometric methods. The broad classes are: clay, silt, and sand ranging from the smaller to

the larger of the less than 2 mm mineral soil fraction. The physical behaviour of a soil is

influenced by the size and percentage composition of the size classes.

Percent passing sieves are used to classify the soil in the Engineering Classifications and to

make judgments on soil properties and performance. Many soil characteristics are influenced

by the distribution of grain sizes for the soil as well as the soil‘s mode of deposition, stress

history, density, and other features (Obioha, 2001). Measurements of percentage passing

involve sieve analysis for the determination of grain size distribution of that portion of the

soil having particle diameters between 3 inches and 0.074 mm (no. 200 sieve).The larger the

numerical value of Cu (coefficient of uniformity), the more the range of particles. Soils with

a value of Cu less than 2 are uniform soils and sands with a value of Cu of 6 or more are well

graded (Arora, 2008). Coefficient of uniformity is an index value showing the average slope

7

of grain size distribution in a soil (William,1982).A uniform soil has low Cu of 1 to 4 while,

a well graded soil has high cu of 6 or more.

2.2.1 SAND PERCENTAGE

Sand percentage is the weight percentage of the mineral particles less than 2 mm and greater

than or equal to 0.05 mm in equivalent diameter in the less than 2 mm soil fraction. The sand

separates recognized are: very coarse, coarse, medium, fine and very fine. Physical properties

of the soil are influenced by the amounts of total sand and of the various sand fractions

present in the soil. Sand particles, because of their size, have a direct impact on the porosity

of the soil. The degree of grittiness in a wet soil sample, when worked between the thumb

and forefinger, gives an estimate of the sand content. The size of sand grains may be

observed with the naked eye or with the aid of a hand lens (Obioha, 2003).

2.2.2 SOIL COLOUR

Soils come in a wide range of colours—shades of brown, red, orange, yellow, grey, and even

blue or green. Colour alone does not affect a soil, but it is often a reliable indicator of other

soil properties. In the surface soil horizons, a dark colour usually indicates the presence of

organic matter in high amount. Soils with significant organic material content appear dark

brown or black. Soils frequently saturated by water appear grey, blue, or green because the

minerals that give them the red and yellow colours have been leached away. Obioha (2001) is

of the view that, Colour and ―feel‖ are the major properties used to estimate the amount of

organic matter.

2.2.3 SOIL TEXTURE

Soils texture depends on its content of the three main mineral components of the soil: sand,

silt, and clay. Texture is the relative percentage of each particle size in a soil. Texture

differences can affect many other physical and chemical properties and are therefore

8

important in measures such as soil productivity. Soils with predominantly large particles tend

to drain quickly and have lower fertility. Very fine-textured soils may be poorly drained.

Soils with a medium texture and a relatively even proportion of all particle sizes are most

versatile. A combination of 10 to 20 percent clay, along with sand and silt in roughly equal

amounts, and a good quantity of organic materials, is considered an ideal mixture for

productive soil (Encarta, 2007).

2.2.4 SOIL AGGREGATE AND STRUCTURE

Individual soil particles tend to be bound together into larger units referred to as aggregates

or soil peds (Obioha, 2001). Aggregation occurs as a result of complex chemical forces acting

on small soil components or when organisms and organic matter in soil act as glue binding

particles together.

Soil aggregates form soil structure, defined as the physical constitution of a soil material as

expressed by the size, shape and arrangement of the solid particles to form compound

particles(Murthy,2009).The measure of strength or grade refers to the stability of the

structural unit and is ranked as weak, moderate, or strong.

2.3 EFFECT OF PETROLEUM SPILL ON THE ENVIRONMENT

The toxicity of crude oil or petroleum products varies widely, depending on their composition

and concentration, environmental factors and the biological state of the organisms at the time

of the contamination. Petroleum distillates up to and including gas oils are more severely

toxic on a short time scale than the other components of crude oil. In heavily polluted areas,

there are immediate detrimental effects on plant and animal life, including agriculture (Baker,

1970; Steinhart and Steinhart, 1972; Rowell, 1977; Fagbami et al., 1988; NDWC., 1995).

9

Nevertheless, different species and different life stages of organisms have different

susceptibilities to pollution (Nelson-Smith, 1973).

The effect of oil on microbial populations depends upon the chemical composition of the oil

and on the species of microorganisms present. Populations of some microbes increase.

Typically, such microbes use the petroleum hydrocarbons as nutrients. The same crude oil

can favour different genera at different temperatures (Westlake et al., 1974). However, some

crude oils contain volatile bacteriostatic compounds that must degrade before microbial

populations can grow (Atlas and Bartha, 1972; Atlas, 1975).

On the other hand, some microbial populations decrease or show a neutral response to

petroleum hydrocarbons. The overall effects of petroleum hydrocarbons on total microbial

diversity remain unclear.

2.3.1 EFFECT ON PLANT

Reports of the effects of oil spills on plants accounts of the death of mangroves, sea grasses

and large inter tidal algae. Recovery from the effect s of oil spills on most local plant

population can require few weeks to 5 years, depending on the type of oil, condition of spills

and species affected. Complete recovery by mangrove forests could require 10 to 15 years

(Albers, 1995).

2.3.2 EFFECTS ON GEOTECHNICAL PROPERTIES OF SOILS

Crude oil spill has great effect on the geotechnical behaviour of the soil according to a study

by Rehman etal., (2007), contamination of clays with crude oil caused an increase in the

Atterberg limits as well as plasticity index .Contrary to Rehman etal (2007) result, increase in

oil contamination resulted to a decrease in the Atterberg limits of the clay (Khamehchiyan

10

etal., 2007). It can also be seen that the liquid limit depends on physiochemical factors (Acar

and Olivieri, 1990).

2.3.3 EFFECTS ON SANDY SOILS

Khamechiyan, etal., (2007) studied the effect of crude oil pollution on poorly graded and silty

sands of Bushehr in Southern Iran with pollution rate of 2%,4%,8%,12% and 16% by dry

weight of the soil samples. On carrying out compaction tests, direct shear tests, uniaxial

compression tests and permeability tests, they found out that increase in the oil content

caused a continuous reduction in maximum dry density with increasing oil contamination up

to about 4% and a reduction in maximum dry density with increased oil content. An

evaluation of the variation of the shear strength of sand contaminated with 3 different types

of oil (Oman crude oil, engine oil and lamp oil) with varying kinematic viscosities by Shin

and Das (2001) whose results of direct shear tests in determination of the soil friction angles

showed a decrease in the total stress friction angles. This agrees with the results of direct

shear tests performed on oil contaminated sandy soils by Ghaly(2002) which showed a

reduction in angle of friction with the increase of oil percentage. Shin etal., (2002) also

reported the same while Ratnaweera and Meegoda,(2006) concluded that, the shear strength

of granular soil decreases with an increase in pore fluid viscosity.

11

Table 2.0 The Biodegradability of different petroleum products

Biodegradability

Hydrocarbon

Constituents Products constituent is found

More degradable n-butane ,n-pentane and n-octane Gasoline

-do- Nonane Diesel fuel

Less degradable Methyl butane,dimethyl pentane ,Methyl

octane

Gasoline

-do- Benzene,toluene, ethylbenzene, xylene Diesel, kerosene

-do- Propylbenzene Diesel

-do- Didecane Heating fuels

-do- Tridecanes Lubricating oils

-do- Tetradecanes Diesel

-do- Naphtalenes Kerosine

-do- Fluoranthene Heating oil

-do- Pyrenes, Aeenaphthenes Lubricating oils

(Source: EPA 1994)

2.4 CONCEPT OF BIOREMEDIATION AND HISTORY

Bioremediation can be defined within the context of biodegradation, a naturally occurring

process whereby bacteria or other microorganisms alter and break down organic molecules

into other substances, eventually producing fatty acids and carbon dioxide (Hoff, 1993).

Bioremediation is the acceleration of this process through the addition of exogenous

microbial populations, through stimulation or manipulation of the contaminated media using

techniques such as aeration or temperature control (Atlas, 1995; Hoff, 1993; Swannell

12

etal,1996). The process of using microorganisms for oil spill cleanup is known as

bioremediation (Coulon etal, 2006)

The term bioremediation is not a new concept; microbiologists have studied the process since

1940.The history of bioremediation in spill response can be divided into three development

periods (Hoff, 1993): the ‗courtship‘ period until 1989, the ‗honeymoon‘ period from 1989

until 1991 and the ‗establishment‘ period since 1992.

In order for bioremediation to be successful, specific conditions are required such as

microorganisms capable of degrading the undesired pollutant. For bioremediation to be

effective they must be the presence of oxygen to create a favourable environment for

microbes. In addition, nutrients, water and adequate soil conditions are important for

bioremediation to be effective. It is important to consider the pollutant, site hydrology,

microbiology, soil condition, temperature, climate and chemistry (Travis, 1990).

2.4.1 BIOLOGICAL PROCESS OF BIODEGRADATION

In general, it is widely accepted that, microbial growth rates are a function of temperature.It

has been found that by providing these microbes with nutrients and oxygen, biodegradation

rate can be maintained. Oil degrading microorganisms are widely distributed in arctic soils

(Travis, 1990).

Fungi and bacteria can be genetically engineered to detoxify man-made pollutants. Most

fungi and bacteria that degrade petroleum hydrocarbons require free or dissolved oxygen

(Odu,1981). The induction of a wild type strain by a mutagen such as acridine orange may

lead to enhanced ability to degrade oil but it may be difficult to get rid of the mutant

population after the desired effect (Obire, 1990).

13

2.4.2 PETROLEUM HYDROCARBON DEGRADING MICROORGANISMS

When an environment is contaminated with petroleum, the hydrocarbon degrading

microorganism increases rapidly. The proportion of hydrocarbon degrading bacterial

populations in hydrocarbon contaminated environments often exceed 10% of the total

bacterial population (Atlas, 1995).

Petroleum contains a wide range of organic compounds that are nutrients for microorganisms.

Petroleum degradation is primarily an oxidation process, although there is some evidence for

anaerobic hydrocarbon degradation (Gutnick and Rosenberg, 1977). Microbes capable of

degrading petroleum hydrocarbons share the following characteristics:

Efficient hydrocarbon uptake via special receptor sites for binding hydrocarbons and/or

unique compounds that assist in the emulsification and transport of hydrocarbons into the

cell.

Filamentous fungi, yeasts, actinomycetes and bacteria all have the ability to utilize

hydrocarbon substrates—though their ability to do so vary among individual strains and, in

some cases, depends on hydrocarbon chain length (Rowell, 1977; Walker et al., 1973). For

instance, bacteria and yeasts showed decreasing abilities to degrade alkanes with increasing

chain length. Filamentous fungi did not exhibit preferential degradation for particular chain

lengths (Walker et al. 1973). In addition to degrading hydrocarbons directly, fungal mycelia

can penetrate oil, thereby increasing the surface area available for biodegradation and

bacterial attack. Fungi can also grow under environmentally stressed conditions such as low

pH and poor nutrient status, where bacteria growth might be limited (Davis and Westlake,

1979).

14

There are approximately 70 genera of known oil-degrading microorganisms, including

bacteria such as Achromobacter, Acinetobacter, Actinomyces, Bacillus, Burkholderia,

Exiguobacterium, Klebsiella, Microbacterium, Nocardia, Pseudomonas, Spirillum,

Streptomyces and Vibrio, and fungi or yeast such as Allescheria, Aspergillus, Candida,

Debayomyces, Mucor, Penicillium, Saccharomyces and Trichoderma. Under natural

conditions, these microorganisms in most areas comprise very few, compared with the total

number of identified microorganisms. However, at petroleum hydrocarbon polluted sites,

these populations may grow and increase because they use petroleum hydrocarbon as a

carbon source (Ahn et al., 1999; Aldrett et al., 1997; Altas, 1981; Bento et al., 2005; Chaerun

et al., 2004; Das and Mukherjee, 2007; Gallego et al., 2001).

Table 2.1 Microorganisms capable of degrading petroleum hydrocarbons

Bacterial Yeast/Fungi

Achromobacter Aspergillus

Acinetobacter Candida

Arthrobacter Cladosporium

Bacillus Penicillium

Flavobacterium Rhodotorula

Nocardia Sporobolomyces

Pseudomonas Trichoderma

Vibrio

Brevibacterium

Corny bacterium

Alcaligences

Source: (Atlas, 1984, Focht and Westlake etal, 1987).

15

2.4.3 HOW BACTERIA FUNCTION AND ADAPT TO ENVIRONMENTAL

CONDITIONS

Bacterial cells, like all cells, require nutrients to carry out their work. These nutrients must be

water soluble to enter through pores in the cell wall and pass through the cell membrane into

the cytoplasm. Bacterial cells use nutrients for a variety of life-sustaining biochemical

activities known collectively as metabolism (Encarta, 2007)

Bacteria easily adapt to environmental stress, usually through changes in the enzymes and

other proteins they produce. These adaptations enable bacteria to grow in a variety of

conditions. Gradual exposure to the stress may enable bacteria to synthesize new enzymes

that allow them to continue functioning under the stressing conditions. Some bacteria that

live in extremely acidic conditions can pump out acid from their cell.

Some kinds of bacteria thrive in hydrothermal vents in oil reservoirs within earth, at high

pressures and temperatures as high as 120oC (250

oF). Other bacteria have adapted to grow in

extremely alkaline or extremely salty conditions. Still others can grow in the total absence of

oxygen. Bacteria able to function in these extreme conditions generally cannot function under

conditions we consider normal (Encarta, 2007).

2.4.4 BACTERIA REPRODUCTION AND SURVIVAL

Bacteria reproduce very rapidly. Replication in some kinds of bacteria takes only about 15

minutes under optimal conditions. In the absence of sufficient nutrients, however, many

bacteria form dormant spores that survive until nutrients become available again. Spore

formation also enables these bacteria to survive other harsh conditions.

16

2.5 GROWTH CYCLE OF MICROORGANISM AND REPRODUCTION

Most microorganisms grow on bio film which provides a protective environment for the

micro colonies of bacteria attached to the particle surface.

The growth pattern of microorganisms can generally be described in five phases; the lag

phase, exponential or growth phase, stationary phase, declining growth and death phase. The

microbial population is measured in terms of number of viable micro organisms (Boyd, 1984)

.It should be noted that in any specific population, not all cells are in the same phase of the

growth curve at any time. Some cells may be in the growth phase while others are in the

death phase. Different microorganism do not all have identical growth curves, each species

has its own specific growth curve. The growth curve for species will not be identical for

different environmental conditions.

2.5.1 LAG PHASE

The lag phase represents the period of adjustment for microorganisms to change to a new

environment. The microbes must adapt to new growth conditions. They first synthesize the

molecules necessary for growth and replication. The length of the lag phase is related to the

amount of biosynthesis micro organisms must do to adjust to this new condition. The more

biosynthesis the micro organisms need to do, the longer the lag phase will be (Baker and

Herson, 1994).

2.5.2 STATIONARY PHASE

The stationary phase is a period when the number of microorganisms remains constant. The

number of cells actively dividing is equal to the number of cells dying. The limiting

conditions that resulted in the declining growth phase (overcrowding, nutrient reduction,

oxygen absence, etc) are accentuated in the stationary phase (Boyd, 1984).

17

2.5.3 EXPONENTIAL AND DECLINING GROWTH PHASE

The exponential growth phase is a period when the doubling time or the growth rates of the

micro organisms are constant. During this phase, cells divide at a constant rate determined by

their ability to process food and the time required for fission. The log number of cells is

directly proportional to time (Agunwamba, 2000).

The exponential phase can be expressed as:

µ =1/x [x/dt] ……………………………………………………………….2.1

µ is the specific growth rate

x is the mass concentration of bacteria (HUB) present.

dt is difference in time.

The declining growth phase is due to limiting environmental conditions such as:

overcrowding, nutrient and dissolved oxygen depletion, the level of toxic products and high

pH conditions (Gaudy, 1980)

2.5.4 DEATH PHASE

The death phase is when there is a decrease in total microbial populations. The death rate for

the exponential death phase is generally slower than the growth rate in the exponential

growth phase. Cell lysis may occur in the death phase depending on the micro organism

involved.

18

Figure 2.1 Growth Curve adapted from Chu (1997)

2.5.5 KINETICS OF BACTERIA GROWTH

Bacteria growth kinetics is a mathematical expression for description of bacteria growth.

They are used for prediction of the performance of biological processes and translation of

qualitative observations into process parameters for the design and operation of the processes

(Agunwamba, 2000).

One of the most useful equations for description of bacteria growth is the Monod rate

equation which is used to predict the rate of substrate utilization by bacteria for any given

concentration of substrate and applies to a culture of bacteria growing on a single rate

limiting substrate.

Ds/dt =kxs/km+s …………………………………………………………..2.2

19

2.6 FACTORS INFLUENCING HYDROCARBON METABOLISM ON

BIOREMEDIATION

Biodegradation of hydrocarbons by microorganisms represents one of the primary

mechanisms by which petroleum and other hydrocarbon pollutants are removed from the

environment. The fate of petroleum hydrocarbons in the environment is controlled by biotic

factors which influence the rate of petroleum hydrocarbon biodegradation (Atlas, 1984).

2.6.1 TEMPERATURE AND CHEMICAL COMPOSITION OF THE CRUDE OIL

Hydrocarbon biodegradation can occur over a wide range of temperatures. The effects of

temperature are interactive with other factors such as, quality of the hydrocarbon mixture and

the composition of the microbial community (Atlas, 1993). When the temperature is lowered,

the viscosity of the oil is increased which changes the toxicity of the oil depending on its

composition (Zhu et.al, 2001).

Temperature as a limiting factor does not seem to be a problem in tropical and temperate

zones. Disappearance of hydrocarbon contaminants from agricultural land can be correlated

with monthly temperature averages (Dibble and Bartha, 1979); generally, hydrocarbon

biodegradation increases with temperature and peaks around 300C– 40

0C (Mentzer and

Ebere, 1996).

The chemical composition of the oil is another parameter which affects bioremediation

process. If the oil is a heavy crude oil which contains resins and asphaltene compounds, it is

very difficult for microrganisms to degrade compared to lighter crude oils (Venosa etal,

1996).

20

2.6.2 NUTRIENT OF NITROGEN AND PHOSPHORUS

Nutrients are the materials that are used by microorganisms to build a new cellular

component. Microorganisms require nutrient of nitrogen and phosphorus for incorporation

into biomass. Nitrogen is a major constituent of nucleic acids. Nucleic acid is responsible for

any organisms‘ ability to reproduce (Gaudy and Gaudy, 1988). Generally, other forms of

nitrogen need to be converted into ammonia before they can be assimilated by micro

organisms (Boyd, 1984). Over fertilization of the soil can depress microbial activity

(Braddock etal., 1997). Numerous applications of small amounts of nitrogen in certain

situations would result in higher biodegradation rates than application of large amount.

Brady and Weil (1999) made a similar observation and concluded that, during

biodegradation, nitrogen may be lost to the atmosphere when nitrate ions are converted to

gaseous forms of nitrogen by a series of widely-occurring biochemical-reduction reactions,

brought about by denitrifying bacteria, such as pseudomonas, bacillus and micrococcus,

especially when localized micro-sites of low oxygen exist well within the soil aggregates.

Phosphorus is required for the production of cellular material. Since it is an essential

constituent of nucleic acids, it is required by the soil. Phosphorus is also a component of

nucleotides such as adenosine triphosphate which is involved in the capture and transfer of

energy in all living systems (Boyd, 1984).

Concentrations of nitrogen and phosphorus severely limit the rates of microbial hydrocarbon

degradation (Barther and Atlas, 1987). High levels of Phosphorus and low levels of nitrogen

indicate that, the N.P.K ration is not balanced and needs organic amendments using low salt

manure to improve the soil structure. Availability of nitrogen and phosphorus which allows

the necessary increase in the size of the hydrocarbon degrading microbial population is an

important factor for bioremediation

21

Table 2.2 Class of phosphorus

Available Phosphorus (Ppm) Class

less than 8.0 Low

8.0-20 Medium

greater than 20 High

Source: Black, 1965.

2.6.3 OXYGEN REQUIREMENT

Aerobic micro organisms need oxygen for metabolism. The availability of oxygen in soils is

dependent on the type of soil. The energy released when a microorganism uses oxygen as the

terminal electron acceptor is twice when compared to nitrate and order of magnitude higher

than sulphate and carbon dioxide (Dupont, 1993).

Huesemann and Truex (1996) and Heuckeroth etal, (1995) have found that for soil

respirometry experiments involving hydrocarbon contamination, the oxygen consumption

rate remained constant as long as the concentration of oxygen was above approximately 5%.

Most fungi and bacteria that degrade petroleum hydrocarbons require free or dissolved

oxygen (Odu, 1981).

2.6.4 MOISTURE CONTENT AND SURFACE AREA

The moisture content of a soil is expressed in terms of the percentage of its water holding

capacity (Atlas, 1998). Moisture is essential for life processes but excess of it in the soil

interferes with the availability of oxygen. The moisture content at which water no longer

flows from soil under the force of gravity is called the field capacity. A moisture content of

approximately 50 to 60% field capacity is optimal for bioremediation in most soils (Cookson,

1995). Too low moisture content can reduce the rate of bioremediation as observed by

22

(Dupont, 1993). Too high moisture content limits the oxygen distribution thus restricting

diffusion of oxygen through the water phase (Baker and Herson, 1994). The partial coating of

the soil surfaces by the hydrophobic hydrocarbons reduces the water holding capacity of the

soil (Atlas, 1981).Water in soil is required for successful bioremediation and is essential for

microbial growth. Microorganisms need water for diffusion of oxygen into the environment

to assimilate nutrients and carry many of the soluble nutrients necessary for microbial growth

(Travis, 1990).

Soil that is hydrated with 50% to 80% of the maximum water-holding capacity has the

greatest microbial activity (Mentzer and Ebere, 1996). Below that level, osmotic and matrix

forces limit the availability of water to microbes; above that level, the reduction of air space

and oxygen decrease microbial activity.

Another factor responsible for effective bioremediation is the surface area .The growth of oil

degrading micro organisms occurs at the interface of the water and oil .The larger the surface

area of the oil, the larger the growth and increase in number of microbes.

2.6.5 SOIL pH

The pH of the soil determines the type of micro organism during hydrocarbon degradation.

Biodegradation is higher under slightly alkaline than under acidic conditions (Bossert and

Barther, 1984). The optimum pH for biodegradation of hydrocarbons is around pH 6 – 8

(Mentzer and Ebere, 1996). Biodegradation of crude petroleum in an acid soil (pH 4.5) could

be doubled by liming to pH 7.4.

Most bacteria have low tolerance for acidic conditions while fungi are more resistant. One of

the most important aspects of nutrient management is maintaining proper soil pH. The soil

pH affects the availability of nutrient element for plant uptake .As the pH of the soil falls

23

below 6, the availability of Nitrogen, Phosphorus and Potassium becomes restricted. A

principal value of soil pH is the information it provides about associated soil characteristics.

Two examples are phosphorus availability and base saturation. Soils that have a pH of

approximately 6 or 7 generally have the most ready availability of plant nutrients. According

to Rhaman et al., (2003) an iincrease in pH suggests the release of by-products during

hydrocarbon degradation.

Table 2.3 SOIL REACTION pH CLASS RANGE

RANGE CLASS

4.5-5.5 Very acidic

5.5-6.0 Distinctively acidic

6.0-6.5 Acidic

6.5-7.0 Faintly acidic

7 Neutral

7.0-7.5 Faintly alkaline

7.5-8.0 Alkaline

8.0-8.5 Strongly alkaline

8.5-9.0 Extreamly Alkaline

Source: Black, 1965.

2.6.6 ORGANIC MATTER AND CARBON

Organic matter percent is the weight of decomposed plant and animal residue and expressed

as a weight percentage of the soil material less than 2 mm in diameter.

Organic matter influences the physical and chemical properties of soil. The organic fraction

24

influences plant growth through its influence on soil properties, promotes water infiltration,

reduces plasticity and cohesion, and increases the available water capacity. It furnishes

energy to micro-organisms in the soil. As it decomposes, it releases nitrogen, phosphorous,

and sulphur. Soils that are very high in organic matter have poor engineering properties and

subside upon drying.

Organic material in soil consists of animal matter, decaying plant matter, living matter and

the biological / chemical changes of the products of these materials. High organic contents in

soil have negative effect on bioremediation. The organic material may bind the contaminants,

nutrient or act as an oxygen sink (Baker and Herson ,1994). As the percentage of organic

material in the soil increased, the sorption of the contaminant increased and consequently

biodegradation rates decreased (Stehmeier, 1997). Sorption binds contaminant and removes

them from the dissolved phase, possibly making them unavailable for micro organisms to

biodegrade. Soil fertility depends on a high content of organic material, bacteria and fungi in

order to digest the complex organic compound that make up living matter. Bacteria action is

the formation of ammonia from Animal and Vegetable proteins. Other bacteria oxidize the

ammonia to form nitrogen compounds.

Table 2.4 class of organic carbons

Organic Carbon[%] Class

less than 0.87 Low

0.87-1.45 Medium

greater than 1.45 High

Source: Black, 1965

25

2.7 TECHNIQUES OF BIOREMEDIATION

There are several different techniques employed in an oil spill. The idea is to accelerate the

rates of natural hydrocarbon biodegradation. The addition of extra bacteria is called

bioaugumentation. Once the bacteria are chosen, the Engineer must carefully meet their

nutritional needs by choosing the correct mix of fertilizer (Irwin, 1996).

2.7.1 BIOAUGMENTATION PROCESS

Bioaugmentation process is the addition of microorganism capable of degrading the toxic

hydrocarbons in order to achieve a reduction of the pollutant (Atlas etal., 1993). One

approach often considered for the bioremediation of petroleum pollutants after an oil spill is

the addition of microorganisms that are able to degrade hydrocarbons. Microorganisms

considered for seeding are obtained by enrichment cultures from previously contaminated

sites. Most tests have indicated that seed cultures are likely to be of little benefit over the

naturally occurring microorganisms at a contaminated site for the biodegradation of the

petroleum contaminants (Atlas, 1995).

The process of introducing pollutant degrading bacteria to contaminated site has been

reported with mixed success (Van veen etal, in Agunwamba etal, 2000).

2.7.2 BIOSTIMULATION PROCESS

Biostimulation process requires the addition of nutrients to the contaminated environment. It

has been found that, when using nitrogen for the supplemental nutrient, a maximum growth

rate is achieved by the oil degrading micro organism at a concentration of 2.0mg/l (Boufadel

etal, 2006).

26

2.8 IMPORTANCE AND ADVANTAGES OF BIOREMEDIATION

Bioremediation has many advantages over traditional cleanup methods of marine oil spills.

One of the major advantages is that, it saves time and cost put in by workers to clean a

contaminated site. The Exxon Valdex spill has proven that the cost of cleaning 120km of

shoreline by bioremediation was less than cost to provide physical washing of the shore for

one day (Atlas, 1995 as referenced by Zhu etal., 2001). Bioremediation is also,

environmentally friendly because it allows natural organisms to degrade the toxic

hydrocarbons into simple compounds without posing treat to the environment (Venosa etal,

1996).

2.8.1 DISADVANTAGES OF BIOREMEDIATION

Although, the benefits of bioremediation cannot be ruled out it has its major disadvantages. It

is a slow process and it is very difficult to conduct field tests due to many factors and

condition which cannot be controlled in the field but only in the laboratory test (Zhu etal.,

2001).

27

CHAPTER THREE

3.0 MATERIALS AND METHODS

3.1 SAMPLE COLLECTION

The six samples used for this study were collected randomly from different locations in

Nsukka (Sample A, B, G, H, I and J).

Nsukka is situated 60

52‘N and 7

024‘E, 70km North of Enugu, the capital of Enugu State. The

mean annual values of rainfall, temperature and relative humidity are; 1678mm, 27.10c and

75.5% respectively. Topographically, the ground Elevation ranges from 280m to 530m above

mean sea level.

All the samples were collected in sterile polythene bags and were well preserved before

analysis.

The Crude oil was obtained from Nigeria National Petroleum Corporation (NNPC) Warri,

Delta State. The Cultured Pseudomonas was obtained from Microbiology Department UNN.

The Fertilizer (NPK 15:15:15) and Cow dung were purchased from the Market in Nsukka

Town.

Apparatus used for the Experiment

Electrical Oven

Set of Sieves and mechanical sieve shaker

Electronic weighing balance/beam balance

pH metre, porcelain vessel, zinc sheet ,mortar /pestle, Beakers, flat glass plate, Erlenmeyer

flask, stop watch, thermometer, desiccators and Refrigerator

28

Spectrophotometer [HACH D2500]

Reagents, chemicals/crude oil used for the experiment

Distilled water, Escravos sweet crude oil, xylene sodium chloride, potassium chloride,

Ammonium sulphate, hydrogen phosphate, calcium chloride,

Microorganism and nutrient used

Pseudomonas bacteria culture

Cow dung

Fertilizer (NPK 15:15:15)

3.2 EXPERIMENTAL PROCEDURES

The air dried and pulverized soil samples were sieved and classified into X and Y such that

X is fine to coarse sand and Y is very fine to coarse sand according to U.S Bureau and PRA

soil classification system respectively(X Sieves no :8, 10,16,36,44,60,120 & 150 and Y

Sieves no 8,16,30,44,60,85,120,150,170, 240 &300) The soil particles retained on each sieve

was poured into the plastic containers (6 plastic container per specimen total of 12 plastic

containers for both specimens)

Escravous sweet crude oil of about 4% was sprinkled on each soil sample at a constant rate

for each soil sample and their moisture content determined immediately after pollution. The

soil samples were left for 4ays before the commencement of treatment process. Prior to the

treatment, the physiochemical and microbial analysis was tested.

The treatment process, commenced after 4days of pollution. This involves the application of

about 100g of NPK 15:15:15 Fertilizer and about 250g of wet cow dung together with 2ml

of pseudomonas which was inoculated in a nutrient broth into the polluted soil samples. The

29

soil samples were homogenously mixed by turning (aerobic condition) after the application.

Samples were collected weekly for a period of 42 days to be tested for, chemical and

microbial analysis under aerobic condition.

The parameters determined includes: moisture content, particle size distribution (Cu and

D50), soil pH, organic carbon content, available nitrogen, available phosphorus, and total

hydrocarbon content, total heterotrophic bacteria count (THBC) and total heterotrophic fungi

count (THFC).

3.2.1 PHYSICAL PARAMETERS

Moisture content

Particle size distribution (Cu, D50 and D10)

3.2.2 CHEMICAL PARAMETERS

Soil pH

Organic carbon content

Available nitrogen

Available phosphorus

Total hydrocarbon content

3.2.3 MICROBIAL PARAMETERS

Total heterotrophic bacteria count (THBC)

Total heterotrophic fungi count (THFC)

30

3.3 CLASSIFICATION TEST

These tests performed were used to provide information on the particle /grain size

distribution of the samples obtained from various locations.

3.3.1 VISUAL DESCRIPTION OF SOIL COLOUR AND STRUCTURE

Soil colour identification and structure of the soil samples were carefully observed by visual

means and hand feels in order to provide relevant information which will help in presenting

parameters used in describing the physical properties of the soils .These provided necessary

information for identification and classification of soils.

3.3.2 SIEVE ANALYSIS

The following sieve numbers were used for sieve analysis:8,10,16,36,44,60,120 &150 for

specimen X while sieve numbers :8,10,16,30,44,60,85,120,150,240,&300 were used for

specimen Y. About 600g of soil samples was weighed. The sieves were stacked with the

largest opening at the top and smallest opening at the bottom. The soil was poured into the

top of the sieves and covered. The set of sieves were put into a mechanical shaker for 15

minutes. When the shaker had stopped, the stacks of sieves were removed. The soil retained

on each sieve was weighed starting with the top sieve to the least sieve size. The retained soil

samples on each sieve were poured into a plastic container. The sieve analysis was repeated

with the same procedures for all other soil samples.

3. 3.3 SOIL pH IN DISTILLED WATER

With a soil to water ratio of 1:1, the following steps were taken: 20g sample of the soil was

put into a 50ml beaker containing 20ml of distilled water. The mixture was allowed to stand

for 30minute, stirring occasionally with a glass rod. The electrodes of the pH meter were

inserted into the partially settled suspension and the pH measured.

31

3.3.4 TOTAL HYDROCARBON CONTENT

Five grams of each soil sample was weighed out and transferred into a 100ml volumetric

flask. Into this, was added 25ml of xylene. The xylene and soil mixture was shaken

vigorously for 5 minutes and filtered into a 100ml flask. The flask and solid materials were

rinsed properly with 20ml xylene and filtered again to a measuring cylinder and the final

volume made up to 50ml. 10ml of the xylene oil extract was placed on corvette and its

absorbance was determined using HACH DR 2500 Spectrophotometer. A calibration curve

was obtained by measuring the absorbance of dilute standard solutions of crude oil prepared

by diluting 2.5, 5.0, 10.0, 20.0, 25.0 and 30.0 micro litre of the oil with 50ml xylene solution.

THC was calculated after reading the absorbance of the extract from the spectrophotometer at

a wavelength of 425nm

3.3.5 AVAILABLE NITROGEN USING H2O2/KCL EXTRACTION

About 5g of air dried soil sample was placed in a 300ml Erlenmeyer flask thereafter, 50ml of

25% H2O2 was added and homogenized manually. The suspension in the flask

(Soil+H2O2) was put in a ventilated oven with a temperature of about 60oC for 3hrs.After

incubation, the suspension was allowed to cool and 150ml of 1MKCL was added. The new

suspension was homogenized for 30 minutes and filtered. The filtrate was then made up to a

final volume of 200ml in a volumetric flask and the NH3 –N was measured in the filtrate.

3.3.6 AVAILABLE PHOSPHORUS

About 10g of air dried soil pulverised to pass 10 mesh sieves was weighed and put in 125ml

plastic extraction Erlenmeyer flask, 40ml of 0.5N NaHCO3 extraction solution was added

into an extraction vessel and was placed on oscillating mechanical shaker for 30 minutes.

32

Thereafter, the suspension was filtered immediately within 1minute.The phosphorus was then

determined from the solution of 50ml final volume.

3.3.7 TOTAL ORGANIC CARBON

About 2g of soil was weighed into 50ml Erlenmeyer flask, 10ml of dichromate sulphuric acid

digestion solution including a reagent blank without soil was pipetted. The Erlenmeyer flask

was covered to minimise loss of chromic acid. This was placed in the digestion oven and

heated to a temperature of 90o c for 90 minutes. The samples were removed from the oven

and allowed to cool for 5 to 10 minutes thereafter, the watch glass was removed and 25ml of

water added. The suspension was mixed thoroughly by blowing air through the suspension

via the 25ml pipette used to add water .The suspension was allowed to stand for 3hrs and

made up to 50ml final volume.10ml was then transferred into a calorimeter cell, and the

calorimeter with the reagent blank at 645nm was zeroed.

3.3.8 TOTAL HETEROTROPHIC BACTERIA COUNT

1g of each soil sample was serially diluted (101

to 107).1ml aliquots from dilution of 10

5 were

plated in duplicate on a nutrient agar using pour plate technique. Incubation was carried out at

370 c for I day.

3.3.9 TOTAL HETEROTROPHIC FUNGI COUNT

1g of each soil sample was serially diluted (101 to 10

7 ).1ml aliquots from dilution 10

5 were

plated in duplicate on dextrose agar plate using the pour plate method, incubation was carried

out at 28o c for a period of 7 days.

33

3.3.10 STATISTICAL ANALYSIS

The least square method of linear regression was used to show the relationship between the

parameters (pH, MC, THC, TOC, AN, AP, THBC, and THFC) with time using the least

square linear regression equation.

3.4 EQUATIONS FOR THE CALCULATION OF PARAMETERS

3.4.1 MOISTURE CONTENT (%)

The moisture content of each soil sample can be calculated by applying;

Moisture content =1

100

d

s

M

M ---------------------------------------3.1

Ms is mass of wet soil and Md is mass of dry soil in (g)

3.4.2 PARTICLE SIZE DISTRIBUTION

The percentage passing the particle size distribution of each soil sample can be calculated

using the following:

% passing =1

100

retainedsoilofwt.

gsinpassoilofwt.

---------------------------------3.2

% finer =100-C

C – Cumulative percentage of material retained on the sieve

Effective size diameter-D10

Coefficient of uniformity (cu) =D60/D10

Coefficient of curvature (cc) = ( D30)2/D60 XD10

34

D10 -particle size such that 10% of the soil is finer

D30 – particle size corresponding to 30% finer

D60 – particle size such that 60% of the soil is finer

3.4.3 SOIL pH IN WATER

The pH values are read from the pH meter when inserted in 1:1 soil to water ratio.

3.4.4 TOTAL HYDROCABON CONTENT

The Total Hydro Carbon Content of the soil can be computed using the following:

gtakensampleofwt

factordilutionmlextractofvolvaluelmgkgmgTHC

.

./)/(

----------------------------3.3

Dilution factors used are: 2,4,10, and 10000

Volume of extract used =50ml

Wt of sample taken = 2g

% Reduction in THC = Initial THC after pollution –THC at remediation period/THC after

pollution x100/1

3.4.5 TOTAL ORGANIC CARBON CONTENT

The Total Carbon Content can be calculated using the following:

gtakensampleofwt.

factordilutionmlextractofvol.valuemg/lTOC(mg/kg)

--------------------------------3.4

The volume of extract used =50ml

Wt of sample taken =2g

35

Dilution factor =2

3.4.6 AVAILABLE PHOSPHORUS

gtakensampleofwt.

factordilutionmlextractofvol.valuemg/l(mg/kg)P

-----------------------------------3.5

Volume of extract=50ml

Dilution factor =50

Wt of sample taken=10g

3.4.7 AVAILABLE NITROGEN

gtakensampleofwt.

factordilutionmlextractofvol.valuemg/l(mg/kg)N

---------------------------------3.6

Volume of extract =200

Wt of sample taken = 5g

Dilution factor 20 or 40

3.4.8 TOTAL HETEROTROPHIC BACTERIA/ FUNGI COUNT

Cell growth/ cell number count can be derived with the following formula:

Cfu =y x1/v x10-x

----------------------------------------------------------------------------3.7

Cfu= colony forming units

v= volume of inoculums‘ (0.1)

y =average number of colonies

36

1=initial dilution

10-x

= dilution factor (10-5

)

3.5 STATISTICAL ANALYSIS

111yxanao -----------------------------------------------------------------------------------3.8

11

211 yxxaxna

o ---------------------------------------------------------------------------3.9

a 1=slope a 0 =Intercept

37

CHAPTER FOUR

4.0 RESULT AND DISCUSSIONS

The physiochemical and microbial properties analysed before pollution, after pollution and

after remediation were tabulated in Appendices A including their graphical representation

and statistical relationship respectively.

4.1 PARTICLE SIZE DISTRIBUTION PARAMETERS

From the particle size distribution curves as shown in figs 4.1A/B, X is fine to coarse sand

while Y is very fine to coarse sand according to U.S Bureau and PRA soil classification

system. Due to the varied sieve sizes, there were variations in the Cu, D10 and D50 values,

between the two classes (X and Y). The lower the Cu and D50 values, the faster the rate of

hydrocarbon loss in the soil as shown in figs 4.1C/D. Sample G had the lowest Cu value of

2.4 with THC values of 3000mg/kg for fine to coarse sand and Cu value of 2.1 with THC of

2000mg/kg for very fine to coarse sand respectively . Sample H had the highest Cu value of

5.5 for fine to coarse sand while sample B had the highest Cu value of 7 and D50 value of

1mm for very fine to coarse sand. According to a statement by, Arora (2008), the larger the

numerical value of Cu, the more the range of particle size. However, particle size with

reduced Cu value has a lower range of particles. Based on these findings, particle size

distribution parameters have influence on bioremediation.

4.2 MOISTURE CONTENT (M.C)

The moisture reduced after crude oil contamination for all the soil samples ;this is expected

because in polluted soil, water droplets adhere to hydrophobic layer formed which prevents

wetting of the inner part of soil aggregate, a similar observation was made by Ayotamuno et

al (2006).Further decrease in moisture content was observed all through the remediation

38

process, this could be as a result of the metabolic activities of the microbes utilizing the crude

oil causing a decrease in total hydrocarbon content and a decrease in moisture content.

Evaporation due to temperature, aerobic and environmental conditions could also be some

factors responsible for the decrease as shown in table A8A/B from the Tables in Appendices

and Fig 4.2A/B. The linear relationship and correlation coefficient of individual soil samples

are shown in tables A23A/B from the tables in Appendices. At the 42- day remediation,

sample A had the highest value of moisture content value of 8.3% for fine to coarse sand and

6.94% for very fine to coarse sand.

4.3 TOTAL HYDROCARBON CONTENT

The decrease in THC through bioaugmentation and biostimulation act increased the

population of microbes present in the soil. This is in agreement with the report of Atlas

(1984), that addition of nutrients and oil degrading microbes increases the rate of microbial

metabolism of crude oil in the soil. Sample G had the least concentration of THC of 2000 and

3000mg/kg for both fine to coarse sand and very fine to coarse sand respectively. Sample H

had highest concentration of THC value of 6800mg/kg for fine to coarse sand while sample B

had the highest value of 6500mg/kg for very fine to coarse sand at the end of remediation

period.

See Tables A4A/B from the table in Appendices and Fig 4.3A/B. The linear relationship and

correlation coefficient of individual soil samples are shown in Tables A19A/B from the table

in Appendices.

4.4 TOTAL ORGANIC CARBON

After pollution, the TOC of the soil increased and later decreased as remediation progressed.

From the statistical inference, the relationship between the TOC and time showed a negative

39

correlation coefficient(r) as shown in tables A21A/B from the table in appendice This

suggest that, TOC reduced with time, A similar observation was made by Ayotamuno,

Kogbara and Agunwamba (2006). Sample G had the least reduction in TOC of 0.84g/kg for

fine to coarse sand and 0.26g/kg for very fine to coarse sand with increased values of THBC

of 250 and 298cfux105/g this showed that the microbes utilized the nutrients in order to

increase their population. Sample H had a reduction value of 2.93g/kg for fine to coarse sand

while sample B had a value of 2.5g/kg for very fine to coarse sand at the end of remediation.

4.5 SOIL pH

There was an increase in soil pH after 4days of crude oil contamination this agrees with a

report by Brady and Weil (1999).The pH decreased during remediation to the acidic side as

shown in tables A10A/B respectively. A fall in pH under similar condition has been reported

by Okpokwasili and Okore (1991).The decrease could be due to the production of

carboxyclic acids during degradation process (Atlas, 1984). Another observation by Tisdale

and Nelson (1999) of decreased pH during remediation may have resulted from a production

of acid radicals through the process of nitrification of the applied fertilizer. The linear

relationship between pH and

days are shown in Table A25A/B from the table in appendice.

Sample A had the least pH value of 4.85 for fine to coarse sand while the least value for very

fine to coarse sand was noticed in Sample I. The pH value slightly closer to the alkaline side

was noticed in sample G with a value of 5.63 for fine to coarse sand and 5.58 for very fine

to coarse sand at the end of remediation.

4.6 AVAILABLE NITROGEN

The available nitrogen for all the soil samples decreased with time. Ayotamuno and kogbara

(2006) in their report stated that, during biodegradation, an enormous loss of nitrogen can be

experienced through a series of widely occurring biochemical reduction reactions brought

40

about by denitrifying bacteria. See tables A7A/B from the table in appendice and Fig 4.5A/B.

The correlation coefficient (r) was negative as shown in tables A22A/B from the Tables in

Appendices. Sample G had the least value for available nitrogen of 0.07g/kg for fine to

coarse sand and 0.09g/kg for very fine to coarse sand. Samples H had a value of 0.178 g/kg

for fine to coarse sand while sample B had a value of 0.190g/kg for very fine to coarse sand

at the end of remediation. This showed that, the nutrients of nitrogen was utilized more by the

microbes present in sample G compared to other soil samples in order to increase in their

biomass.

4.7 AVAILABLE PHOSPHORUS

There was an enormous decrease in the phosphorus throughout the period of remediation.

Tables A9A/B of appendix A and Fig 4.6A/B showed a decrease in phosphorus. The linear

relationship and correlation coefficient are shown in tables A24A/B from the tables in

appendice. Sample H had the highest value of available phosphorus of 0.790g/kg for fine to

coarse sand while sample B had a value of 0.510g/kg for very fine to coarse sand. Sample G

had the least value of 0.412G/g/kg for fine to coarse sand and 0.238g/kg for very fine to

coarse sand at the end of remediation.

4.8 TOTAL HETEROTROPHIC BACTERIA AND FUNGI COUNT

The total heterotrophic bacteria and fungi counts increased throughout the period of

remediation. This could be that, the crude oil used was favourable to the hydrocarbon

degrading microbes .This is in agreement with a report by Atlas (1984). Ayotamuno and

Kogbara (2006) made a similar observation. See tables A3A/B for THBC and tables A5A/B

for THFC from the tables in appendice. Tables A18A/B and tables A20A/B showed the linear

relationship and correlation coefficient of the individual soil sample. The increase in THBC

was found to be highest in sample G with values of 250cfux105/g for fine to coarse sand and

41

298 cfu x105/g for very fine to coarse sand at the end of remediation. Sample H had the least

value of THFC increase with values of 150 cfu x105/g for fine to coarse sand while sample B

had the least value of 156cfux105/g for very fine to coarse sand at the end of remediation.

42

Fig 4.1A: Particle Size Distribution Curve for fine to coarse sand(X)

Fig.4. 1B: Particle Size Distribution Curve for very fine to coarse sand (Y)

43

Fig 4.1C1: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs Cu for fine to

coarse sand(X)

Fig 4.1C2: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs Cu for very fine to

coarse sand(Y)

44

Fig 4.1 D1: Concentration of Remaining Crude Oil (mg/kg) at last Day Vs D50 for fine to

coarse sand(X)

Fig 4.1 D2: Concentration of Remaining Crude Oil (mg/kg) Vs D50 at last Day for very fine

to coarse sand(Y)

45

Fig 4. 2A: Moisture Content Vs days for fine to coarse sand( X)

Fig 4.2B: Moisture Content Vs days for very fine to coarse sand(Y)

46

Fig 4.3A: Total Hydrocarbon Content Vs Days for fine to coarse sand(X)

Fig 4 .3B : Total Hydrocarbon Content Vs Days for very fine to coarse sand(Y)

47

Fig.4. 4A: Total Organic Carbon Vs days for fine to coarse sand(X)

Fig4. 4B: Total Organic Carbon Vs days for very fine to coarse sand (Y)

48

Fig 4.5A: Available Nitrogen Vs days for fine to coarse sand(X)

Fig 4.5B: Available Nitrogen Vs days for very fine to coarse sand(Y)

49

Fig 4. 6A: Available Phosphorus Vs days for fine to coarse sand (X)

Fig 4. 6B: Available Phosphorous Vs days for very fine to coarse sand(Y)

50

Fig 4.7A: pH Vs Days for fine to coarse sand (X)

Fig 4.7B: pH Vs Days for very fine to coarse sand(Y)

51

Fig4. 8A: Total Heterotrophic Bacteria count cfux105/g Vs Time (days) for fine to coarse

sand(X)

Fig. 4.8B: Total Heterotrophic Bacteria Count cfu x105/g Vs days for very fine to coarse

sand(Y)

52

Fig 4.9A:Total Heterotrophic Fungi Counts cfux105/g Vs days for fine to coarse sand (X)

Fig 4.9B: Total Heterotrophic Fungi Counts cfu x105/g Vs Days for very fine to coarse sand

(Y)

53

Fig 4. 10A: Rate of Hydrocarbon Loss (% Reduction Vs Days for fine to coarse sand(X)

Fig 4.10B: Rate of Hydrocarbon Loss (% Reduction) Vs Days for very fine to coarse sand

(Y)

54

CHAPTER FIVE

5.0 CONCLUSION

Experimental investigations were carried out by varying the sieve sizes and dividing the soil

into two X and Y such that, X is fine to coarse sand while Y is very fine to coarse sand

according to U.S Bureau and PRA soil classification systems.

Crude oil was used to pollute the soil samples prior to remediation. The duration of treatment

was 42 days on application of nutrients and cultured microorganism to the polluted soils.

From the results of this study, it was observed that, the lower the Cu and D50 values, the

faster the rate of hydrocarbon loss. This showed that, particle size distribution parameter has

influence on bioremediation. The correlation coefficient(r) of THC vs Cu for fine to coarse

sand(X) is 0.867 while for very fine to coarse sand it is 0.923.

Microbial biomass of both total heterotrophic bacteria and fungi counts increased on addition

of nutrients of NPK 15:15:15 fertilizer, cow dung and microorganism (pseudomonas) on all

the soil samples although, the nutrients were effectively utilized more by microorganisms

present in soil samples with less Cu and D50 values. Correlation Coefficient (r) value for

THC, TOC, AN, AP, pH and MC was negative in all the soil samples ranging from (r=-0.808

to -0.998 ) while that of THBC and THFC was positive in all the soil samples ranging from

(r=0.901 to 0.997).

However, the contribution to knowledge of this study is that, data obtained from the partcle

size distribution parameters could be used for further bioremediation studies. This makes this

research unique since, such data are yet to be available in literatures on Nigeria soils.

55

5.1 RECOMMENDATIONS

Based on the findings of this research, the following contribution and recommendation would

be found useful in bioremediation practice.

The research recommends that, further studies be carried out on other soil engineering

properties in order to adopt the most suitable method of remediation for a particular spill as

well as cost estimate of the remediation strategy.

Combined practice of bioaugmentation and biostimulation will help improve the rate of

degradation of crude oil polluted soils.

The Government should Encourage Technological researches through the issuance of grants

in order to achieve better output of results.

56

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61

TableA1: Physiochemical and Soil Microbial Characteristics before crude oil pollution

Parameters A B G H I J

Moisture content

% 25.68 27.35 20.42 28 19.45 24.29

colour Black Red Orange Red GREY Grey

texture Sandy sandy sandy sandy sandy sandy

THC mg/kg 20 N.D 40 120 20 60

TOC g/kg 4.407 1.078 1.001 1.451 1.21 1.064

Nitrogen g/kg 0.2 0.238 0.144 0.181 0.152 0.409

phosphorus g/kg 0.196 0.163 0.049 0.163 0.098 0.049

C/N ratio 22 4 7 8 10 3

C/P ratio 22 7 20 9 12 22

N/P ratio 1 1.5 2.9 1 1.5 8

Soil pH 6.32 5.6 7.63 6.08 5.73 5.8

THB CFU

x103/g 250 160 108 88 128 210

THF CFU

x103/g 20 35 4 ND 11 110

62

Table A2A: Soil Physiochemical and Microbial Characteristics four days after pollution for

fine to coarse sand (X)

Parameters A B G H I J

Moisture content

% 22.85 25.29 18.1 23.67 16..95 21.62

D50 0.4 0.5 0.4 0.8 0.25 0.37

CU 3 3.8 2.4 5.5 2.5 2.6

D10 0.18 0.17 0.19 0.18 0.16 0.16

D60 0.55 0.66 0.46 1 0.39 0.41

THC mg/kg 15600 13800 18450 14200 17900 16500

TOC g/kg 7.048 7.457 5.056 6.298 5.821 7.08

Nitrogen g/kg 0.24 0.31 0.13 0.14 0.308 0.424

phosphorus g/kg 0.13 0.815 0.098 0.35 0.3 0.08

C/N ratio 29 24 39 45 19 17

C/P ratio 54 9 52 18 19 88

N/P ratio 2 0.4 1 0.4 1 5

Soil pH 6.4 5.74 6.25 6.9 5.74 6.1

THB CFU

x103/g 295 38 115 140 50 62

THF CFU

x103/g 59 17 ND 68 ND 5

63

Table A 2B: Soil Physiochemical and Microbial Characteristics four days after

Pollution for very fine to coarse sand(Y)

Parameters A B G H I J

Moisture content

% 23 25.95 19.15 25.64 17.83 22.61

D50 0.24 1 0.3 0.6 0.25 0.23

D10S 0.07 0.2 0.19 0.06 0.09 0.09

D60 0.3 1.6 0.4 0.18 0.32 0.35

CU 4.3 8 2.1 3 3.5 3.9

THC mg/kg 15400 13000 18900 14800 14200 15000

TOC g/kg 7.03 7.328 5.01 6.2 5.83 7.08

Nitrogen g/kg 0.243 0.3 0.14 0.176 0.29 0.42

phosphorus g/kg 0.12 0.8 0.098 0.38 0.4 0.7

c/N ratio 29 24 36 35 20 16

C/P ratio 58 9 51 16 14 10

N/P ratio 2 0.4 1.4 0.5 0.7 0.6

Soil pH 6.8 5.7 6.75 7.1 5.74 5.9

THB CFU

x103/g 300 45 115 140 48 68

THF CFU

x103/g 56 19 ND 69 ND 5

Table A 3A: Total Heterotrophic Bacteria Count Vs days for fine to coarse sand (X)

days SP X 7 14 21 28 35 42

CF

U x

10

5/g

A 97 108 118 120 130 150

B 95 104 116 119 125 145

G 110 144 149 153 175 250

H 55 82 100 110 115 122

I 126 135 142 150 170 200

J 114 128 133 140 160 180

64

Table A3B: Total Heterotrophic Bacteria Count Vs days for very fine to coarse sand (Y)

Days SP Y 7 14 21 28 35 42

CF

U x

10

5/g

A 138 155 162 172 183 190

B 122 141 150 163 179 185

G 200 235 248 266 275 298

H 205 213 220 228 239 250

I 184 196 200 214 224 235

J 160 177 189 206 218 220

Table A4A: Total Hydrocarbon Content Vs days for fine to coarse sand(X)

days SP X 7 14 21 28 35 42

Mg/k

g

A 12600 11300 10400 9000 7500 5500

B 13000 11980 10710 9200 8250 6000

G 10500 8400 7800 6900 5000 3000

H 13930 12200 10950 9800 9300 6800

I 11200 9600 8400 7450 6000 4000

J 11900 9920 9000 8900 6800 4600

Table A4B: Total Hydrocarbon Content Vs days for very fine to coarse sand( Y)

days SP Y 7 14 21 28 35 42

Mg/k

g

A 13200 10500 9700 8400 7050 5600

B 12600 11000 9800 8600 7570 6500

G 9800 7000 6200 5000 4500 2000

H 10720 7500 7200 5280 4900 2700

I 11000 7950 6000 5700 5450 3000

J 12250 8450 6300 6000 5670 3700

65

Table A5A: Total Heterotrophic Fungi Count Vs days for fine to coarse sand ( X)

days SP X 7 14 21 28 35 42

CF

U x

10

5/g

A 83 114 130 163 178 200

B 74 106 118 152 162 180

G 150 182 199 220 260 280

H 65 100 113 125 130 150

I 110 135 150 185 200 250

J 98 120 148 173 185 210

Table A 5B: Total Heterotrophic Fungi Count Vs days for very fine to coarse sand ( Y)

days SP Y 7 14 21 28 35 42

CF

U x

10

5/g

A 93 102 113 120 140 165

B 67 84 98 105 135 120

G 145 178 200 210 223 230

H 120 140 162 180 192 200

I 100 122 138 155 164 182

J 99 113 125 143 150 170

Table A6A: Total Organic Carbon Vs Days for fine to coarse sand ( X)

days SP X 7 14 21 28 35 42

g/k

g

A 5.06 4.02 3.67 3.5 3 2.32

B 5.18 4.32 3.83 3.74 3.61 2.45

G 3.45 3 2.26 1.92 1.0 0.84

H 5.42 4.6 4.02 3.99 3.85 2.93

I 3.87 3.2 2.65 2.06 1.17 0.92

J 4.98 3.9 3.5 3.37 2.9 1.95

66

Table A6B: Total Organic Carbon Vs days for very fine to coarse sand (Y)

days SPY 7 14 21 28 35 42

g/k

g

A 4.75 4 3.69 3 2.79 2.1

B 4.81 4.42 4.06 3.98 3.06 2.5

G 3.13 2.26 1.66 1.26 0.78 0.26

H 3.88 2.94 2.4 1.92 0.98 0.9

J 4.28 3.90 3.55 2.99 2.2 1.86

I 3.95 3.55 3.10 2.85 2 1.5

Table A7A: Available Nitrogen Vs days for fine to coarse sand ( X)

days SP X 7 14 21 28 35 42

g/k

g

A 0.215 0.200 0.188 0.170 0.162 0.155

B 0.224 0.215 0.194 0.185 0.170 0.162

G 0.110 0.100 0.098 0.095 0.091 0.07

H 0.26 0.240 0.200 0.190 0.183 0.178

I 0.195 0.192 0.170 0.156 0.150 0.140

J 0.2 0.198 0.182 0.163 0.154 0.149

Table A7B: Available Nitrogen Vs days for very fine to coarse sand (Y)

days SP Y 7 14 21 28 35 42

g/k

g

A 0.460 0.398 0.295 0.270 0.190 0.179

B 0.552 0.497 0.326 0.299 0.250 0.192

G 0.125 0.115 0.108 0.102 0.096 0.09

H 0.142 0.130 0.122 0.119 0.105 0.098

I 0.295 0.200 0.190 0.169 0.155 0.132

J 0.350 0.305 0.280 0.200 0.168 0.143

67

Table A8A: Moisture Content Vs days for fine to coarse sand ( X)

days SP X 7 14 21 28 35 42

%

A 18.89 15.81 11.4 9.54 9 8.3

B 16 12 10 9.25 6.78 5.62

G 14.25 12.98 8.98 8 7.15 7

H 23.8 20.85 16.35 9.76 9.24 4.5

I 12.44 10.58 7.35 5.8 5.65 5.6

J 19.68 13.95 11.25 7.69 8.1 7.92

Table A8B: Moisture Content Vs days for very fine to coarse sand(Y)

days SP Y 7 14 21 28 35 42

%

A 20.95 17.1 11.72 10.2 6.25 6

B 15.75 13.8 10.62 9.85 5.42 5.1

G 17.21 14.21 9.28 8.55 5.97 5

H 25.34 18.25 15.39 10.65 7 6.91

I 16.12 11.38 10.76 6.88 5.39 5.35

J 20.5 19.15 14.77 8.76 7.96 6.94

Table A9A: Available Phosphorus Vs days for fine to coarse sand ( X)

days SP X 7 14 21 28 35 42

g/k

g

A 0.999 0.988 0.893 0.850 0.700 0.684

B 1.000 0.990 0.926 0.897 0.788 0.720

G 0.975 0.946 0.775 0.578 0.428 0.412

H 1.100 0.997 0.989 0.900 0.815 0.790

I 0.986 0.968 0.884 0.650 0.488 0.460

J 0.992 0.980 0.825 0.710 0.600 0.581

68

Table A 9B: Available Phosphorus Vs days for very fine to coarse sand ( Y)

days SP Y 7 14 21 28 35 42

g/k

g

A 1.015 0.989 0.860 0.690 0.600 0.420

B 1.02 0.994 0.895 0.700 0.680 0.510

G 0.965 0.935 0.740 0.580 0.413 0.238

H 0.984 0.942 0.785 0.620 0.460 0.300

I 0.997 0.963 0.794 0.649 0.473 0.325

J 1.0 0.972 0.820 0.655 0.503 0.382

Table A 10A: pH Vs days for fine to coarse sand (X)

days SP X 7 14 21 28 35 42

Ph

A 6.25 6.1 5.98 5.83 5.78 5.6

B 5.7 5.65 5.62 5.6 5.55 5.4

G 6.2 6 5.97 5.81 5.76 5.63

H 6 5.98 5.88 5.75 5.62 5.5

I 5.68 5.61 5.57 5.46 5.4 5

J 5.95 5.9 5.72 5.6 5.35 5.2

Table A 10B: pH Vs days for very fine to coarse sand( Y)

day SP Y 7 14 21 28 35 42

Ph

A 6 5.97 5.61 5.45 5.4 4.85

B 5.6 5.4 5.38 5.3 5.24 5.2

G 6.6 5.85 5.7 5.62 5.6 5.58

H 6.98 5.92 5.84 5.64 5.49 5.38

I 5.72 5.67 5.55 5.47 5.4 5.27

J 5.88 5.86 5.7 5.64 5.5 5

69

Table A 11A: Rate of Hydrocarbon Loss Vs days for fine to coarse sand (X)

days SP X 7 14 21 28 35 42

%

A 19 28 33.33 42.31 52 64.74

B 6 13.2 22.4 33.3 40.22 56.52

G 43.1 54.5 57.7 62.6 72.9 83.74

H 3 14 22.9 30.98 34.51 52.11

I 37.43 46.4 53 58.37 66.5 77.65

J 27.9 39.88 45.45 46.1 59 72

Table A11 B : Rate of Hydrocarbon Loss Vs days for very fine to coarse sand(Y)

days SP Y 7 14 21 28 35 42

%

A 14.3 32 37 45.45 54.22 63.64

B 3.08 15.4 25 33.85 42 53.85

G 48.15 62.96 67.2 73.54 76.2 89.42

H 27.56 49.32 51.35 64.32 66.89 81.76

I 22.53 44 57.74 59.86 61.6 78.87

J 18.3 43.67 58 60 62 75.3

70

Table A A1: Particle Size Distribution OF sample A for fine to coarse sand ( X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 420 464 44 466 91.37

1.8 410 425 15 451 88.43

0.9 400 443 43 408 80

0.5 340 490 150 258 50.6

0.36 326 392 66 192 37.65

0.25 310 318 8 184 36.08

0.15 298 450 152 32 6.27

0.1 295 309 14 18 3.53

PAN 432 450 18 0 0

Table A A2: Particle Size Distribution of sample A for very fine to coarse sand( Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

1.8 418 460 42 566 93.09

0.9 410 448 38 528 86.84

0.5 400 490 90 438 72.04

0.36 355 438 83 355 58.39

0.25 320 360 40 315 57.81

0.2 308 430 122 193 31.74

0.15 295 330 35 158 25.99

0.1 302 315 13 145 23.85

0.09 298 306 8 137 22.53

0.065 291 325 85 52 8.55

0.05 240 285 45 7 1.15

PAN 432 439 7 0 0

71

Table A B1: Particle Size Distribution of Sample B for fine to coarse sand( X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 414 470.1 56.1 445.5 88.82

1.8 401.05 434.85 33.8 411.7 82.08

0.9 395 447.9 52.9 358.8 71.53

0.425 338.65 481.7 143.05 215.75 43.01

0.36 330 410 80 135.75 27.06

0.25 323 330.5 7.5 128.25 25.57

0.15 304 398.3 94.1 34.15 6.81

0.1 312 320.8 8.3 25.85 5.15

PAN 430 455.95 25.85 0 0

Table A B2: Particle Size Distribution of Sample B for very fine to coarse sand ( Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 430 565 135 411 75.27

0.9 401 490 89 322 58.97

0.5 395 467 72 250 45.79

0.3 378 470 92 158 28.94

0.25 335 370 35 123 22.53

0.2 323 330 7 116 21.24

0.15 300 370 70 46 8.42

0.1 302 310 8 38 6.96

0.09 295 298 3 35 6.41

0.065 290 320 25 10 0.91

0.05 284 315 5 5 0.84

PAN 415 420 5 0 0

72

Table A G1: Particle Size Distribution of Sample G for fine to Coarse sand ( X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 409 425 16 472 96.72

1.8 398 418 20 452 92.62

0.9 386 420 34 418 85.67

0.5 332 482 150 268 54.92

0.36 326 470 144 124 25.41

0.25 315 339 24 100 20.49

0.15 297 370 73 27 5.53

0.1 305 320 15 12 2.46

PAN 428 440 12 0 0

Table A G2: Particle Size Distribution of Sample G for Very Fine to Coarse Sand ( Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 412 436 24 643 96.4

0.9 400 415 15 628 94.15

0.5 390 430 40 588 88.16

0.3 402 510 108 480 71.96

0.25 350 500 150 330 49.47

0.2 310 315 5 325 48.73

0.15 306 500 194 131 19.64

0.1 298 406 108 23 3.45

0.09 302 305 3 20 2.99

0.065 291 298 7 13 1.95

0.05 242 250 8 5 0.45

PAN 432 435 5 0 0

73

Table A H1: Particle Size Distribution of Sample H for fine to Coarse Sand (X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 409 544 135 368 73.16

1.8 398 427 29 339 67.4

0.9 392 450 58 281 55.86

0.5 338 467 129 152 30.22

0.36 332 365 33 119 23.66

0.25 310 322 12 107 21.27

0.15 306 367 61 46 9.14

0.1 291 310 19 27 5.37

PAN 425 452 27 0 0

74

Table A H2: Particle Size Distribution of Sample H for very fine to coarse sand (Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 408 560 152 434 74.06

0.9 390 460 70 364 62.12

0.5 370 472 102 262 44.71

0.3 330 375 45 217 37.03

0.25 315 328 13 204 34.81

0.2 306 376 70 134 22.87

0.15 310 318 8 126 21.5

0.1 300 310 10 116 19.79

0.09 298 319 21 95 18..26

0.065 285 370 85 10 16.4

0.05 343 350 7 3 1.71

PAN 427 430 3 0 0

75

Table A I1: Particle Size Distribution of sample I for fine to coarse sand (X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 412 430 18 483 96.4

1.8 399 415 16 467 93.21

0.9 394 408 14 453 90.42

0.5 336 406 70 383 76.45

0.36 334 440 106 277 55.29

0.25 324 332 8 269 53.69

0.15 305 533 228 41 8.18

0.1 315 324 9 32 6.39

PAN 428 460 32 0 0

Table A I 2: Particle Size Distribution of sample I for very fine to coarse sand (Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 402 430 28 517 94.86

0.9 395 420 25 492 90.27

0.5 360 445 85 407 74.68

0.3 330 405 75 332 60.91

0.25 320 385 65 267 48.99

0.2 310 464 154 113 20.73

0.15 306 346 40 73 13.39

0.1 300 308 8 65 11.93

0.09 294 296 2 63 11.56

0.065 290 323 33 30 5.5

0.05 280 285 5 25 4.59

PAN 415 440 25 0 0

76

Table A J1: Particle Size Distribution of Sample J for Fine to Coarse Sand (X)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 410 436 26 474 94.8

1.8 400 415 15 459 91.8

0.9 394 417 23 436 87.2

0.5 339 462 123 313 62.6

0.36 332 394 62 251 50.2

0.25 323 363 40 211 42.2

0.15 304 480 176 35 7

0.1 314 330 16 19 3.8

PAN 426 445 19 0 0

77

Table A J2: Particle Size Distribution of Sample J for Very Fine to Coarse Sand(Y)

B.S Sieve

no[ mm]

wt of empty

sieve[g]

wt of

sieve+soil

[g]

wt of soil

retained[g]

wt of soil

passing[g]

% finer

[%]

2.06 398 460 62 478 95.68

0.9 390 445 55 454 89.194

0.5 365 455 90 364 71.51

0.36 330 404 74 290 56.974

0.25 310 315 5 285 55.99

0.212 305 495 190 95 18.66

0.15 300 345 45 50 9.82

0.085 295 312 17 33 6.48

0.063 296 300 4 29 5.69

0.05 285 294 9 20 3.93

PAN 420 440 20 0 0

78

Table A18A: Linear Relationship and Correlation coefficient of days Vs THBC for fine to

coarse sand( X)

S/N Correlation coefficient Linear relationship

A 0.972 Y= 87.2+1.359x

B 0.973 Y= 85.73+1.289x

G 0.901 Y= 83.8+3.253x

H 0.951 Y= 52.93+1.812x

I 0.952 Y= 105.53+1.971x

J 0.973 Y= 99.2+1.767x

Table A18B: Linear Relationship and Correlation coefficient of days Vs THBC for very fine

to coarse sand(Y)

S/N Correlation coefficient Linear relationship

A 0.992 Y= 131.27+1.445x

B 0.993 Y= 112.47+1.804x

G 0.983 Y= 190.99+2.563x

H 0.995 Y= 194.73+1.269x

I 0.994 Y= 173.53+1.441x

J 0.983 Y= 151+1.796x

79

Table A19A: Linear Relationship and Correlation coefficient of days Vs THC (X)

S/N Correlation coefficient Linear relationship

A -0.993 Y= 14213 -197x

B -0.992 Y= 14627 -195x

G -0.984 Y= 11793 -198x

H -0.985 Y= 15047 -186x

I -0.994 Y= 12550 -195x

J -0.970 Y= 13116 -188x

Table A19B: Linear Relationship and Correlation coefficient of days Vs THC ( Y)

S/N Correlation coefficient Linear relationship

A -0.988 Y= 14040 -203x

B -0.997 Y= 13544 -171x

G -0.973 Y= 10520 -195x

H -0.970 Y= 11365 -203x

I -0.944 Y= 11297 -195x

J -0.928 Y= 12201 -210x

80

Table A20A: Linear Relationship and Correlation coefficient of days Vs THFC ( X)

S/N Correlation coefficient Linear relationship

A 0.995 Y= 63.667+3.31x

B 0.988 Y= 58.80+ 2.99x

G 0.994 Y= 124.67 +3.69x

H 0.964 Y= 61.13 +2.15x

I 0.985 Y= 78.67 +3.80x

J 0.995 Y= 77.67 +3.18x

Table A20B: Linear Relationship and Correlation coefficient of days Vs THFC ( Y)

S/N Correlation coefficient Linear relationship

A 0.972 Y=74.07 +1.96x

B 0.930 Y= 59+ 1.73x

G 0.963 Y= 141 +2.33x

H 0.987 Y= 108 +2.34x

I 0.994 Y= 88 +2.26x

J 0.995 Y= 85 +1.97x

81

Table A21A: Linear Relationship and Correlation coefficient of days Vs TOC ( X)

S/N Correlation coefficient Linear relationship

A -0.973 Y= 5.29 -0.069x

B -0.950 Y= 5.44 -0.065x

G -0.989 Y= 4.02 -0.079x

H -0.950 Y= 5.61 -0.060x

I -0.994 Y= 4.45 -0.087x

J -0.967 Y= 5.26 -0.075x

Table A21B: Linear Relationship and Correlation coefficient of days Vs TOC ( Y)

S/N Correlation coefficient Linear relationship

A -0.991 Y= 5.14 -0.072x

B -0.971 Y= 5.38 -0.064x

G -0.991 Y= 3.48 -0.08x

H -0.984 Y= 4.30 -0.087x

I -0.990 Y= 4.91 -0.072x

J -0.988 Y= 4.54 -0.07x

82

Table A 22A: Linear Relationship and Correlation coefficient of days Vs Available Nitrogen

(X)

S/N Correlation coefficient Linear relationship

A -0.989 Y= 0.22 -0. 0017x

B -0.992 Y= 0.24 -0.0018x

G -0.919 Y= 0.12 -0.0009x

H -0.941 Y= 0.27 -0.002x

I -0.981 Y= 0.21 -0.0017x

J -0.978 Y= 0.2 -0.0016x

Table A22B: Linear Relationship and Correlation coefficient of days Vs Available Nitrogen

( Y)

S/N Correlation coefficient Linear relationship

A -0.979 Y= 0.50 -0. 0084x

B -0.968 Y= 0.61 -0.01x

G -0.995 Y= 0.13 -0.0009x

H -0.989 Y= 0.15 -0.0012x

I -0.913 Y= 0.29 -0.0039x

J -0.987 Y= 0.39 -0.0062x

83

Table A22B: Linear Relationship and Correlation coefficient of days Vs Available Nitrogen

(Y)

S/N Correlation coefficient Linear relationship

A -0.979 Y= 0.50 -0. 0084x

B -0.968 Y= 0.61 -0.01x

G -0.995 Y= 0.13 -0.0009x

H -0.989 Y= 0.15 -0.0012x

I -0.913 Y= 0.29 -0.0039x

J -0.987 Y= 0.39 -0.0062x

Table A23A: Linear Relationship and Correlation coefficient of days Vs Moisture Content (

X)

S/N Correlation coefficient Linear relationship

A -0.943 Y=1 9.681 -0. 307x

B -0.975 Y= 16.773 -0.279x

G -0.938 Y= 15.199 -0.223x

H -0.959 Y= 26.559 -0.482x

I -0.923 Y= 12.957 -0.206x

J -0.903 Y= 19.423 -0.326x

84

Table A23B: Linear Relationship and Correlation coefficient of days Vs Moisture Content

(Y)

S/N Correlation coefficient Linear relationship

A -0.973 Y=22.919 -0. 444x

B -0.982 Y= 18.006 -0.323x

G -0.970 Y= 18.687 -0.353x

H -0.970 Y= 26.987 -0.533x

I -0.955 Y= 16.883 -0.309x

J -0.964 Y= 23.751 -0.438x

Table A24A: Linear Relationship and Correlation coefficient of days Vs Available

Phosphorus ( X)

S/N Correlation coefficient Linear relationship

A -0.972 Y=1.10 -0.01x

B -0.971 Y= 1.09 -0.008x

G -0.976 Y= 1.14 -0.019x

H -0.983 Y= 1.15 -0.0089x

I -0.967 Y= 1.17 -0.018x

J -0.977 Y= 1.11 -0.013x

85

Table A24B: Linear Relationship and Correlation coefficient of days Vs Available

Phosphorus ( Y)

S/N Correlation coefficient Linear relationship

A -0.985 Y=1.19 -0.018x

B -0.977 Y= 1.17 -0.015x

G -0.989 Y= 1.18 -0.022x

H -0.991 Y= 1.19 -0.02x

I -0.989 Y= 1.20 -0.02x

J -0.988 Y= 1.19 -0.018x

Table A25A: Linear Relationship and Correlation coefficient of days Vs pH ( X)

S/N Correlation coefficient Linear relationship

A -0.994 Y=6.359 -0.0178x

B -0.933 Y= 5.769 -0.007x

G -0.984 Y= 6.268 -0.015x

H -0.984 Y= 6.159 -0.015x

I -0.906 Y= 5.867 -0.017x

J -0.987 Y= 6.172 -0.022x

86

Table A25B: Linear Relationship and Correlation coefficient of days Vs pH ( Y)

S/N Correlation coefficient Linear relationship

A -0.957 Y=6.309 -0.031x

B -0.954 Y= 5.609 -0.010x

G -0.808 Y= 6.418 -0.025x

H -0.877 Y= 6.824 -0.039x

I -0.994 Y= 5.827 -0.0128x

J -0.912 Y= 6.151 -0.023x

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